# PLANET: Improved Convolutional Neural Networks with Image Enhancement for Image Classification

Authors: Chaohui Tang; Qingxin Zhu; Wenjun Wu; Wenlin Huang; Chaoqun Hong; Xinzheng Niu
Journal: Mathematical Problems in Engineering
 (2020)

---

## Abstract

In the past few years, deep learning has become a research hotspot and has had a profound impact on computer vision. Deep CNN has been proven to be the most important and effective model for image processing, but due to the lack of training samples and huge number of learning parameters, it is easy to tend to overfit. In this work, we propose a new two-stage CNN image classification network, named “Improved Convolutional Neural Networks with Image Enhancement for Image Classification” and PLANET in abbreviation, which uses a new image data enhancement method called InnerMove to enhance images and augment the number of training samples. InnerMove is inspired by the “object movement” scene in computer vision and can improve the generalization ability of deep CNN models for image classification tasks. Sufficient experiment results show that PLANET utilizing InnerMove for image enhancement outperforms the comparative algorithms, and InnerMove has a more significant effect than the comparative data enhancement methods for image classification tasks.


---

## Body

## 1. Introduction

Deep learning [1] maps data of different classes into separate high dimension space by constructing a complex function with a great number of parameters, which are different from those that obtain instance relationship based on set theory [2, 3]. Deep learning accelerates the rapid development of artificial intelligence in all fields of our daily lives and has imposed far-reaching impacts on scientific fields such as computer vision [4, 5] and natural language processing [6], as well as medical image processing [7]. In the whole picture of deep learning, deep convolutional neural networks have been proved to be the most important and effective models for image processing. However, CNNs learn features of images with so many parameters that they could easily tend to be overfitting and so have poor ability of generalization. In such a case, CNNs could learn features of all the training data well but have poor performance on unseen data, i.e., test data.To improve the generalization performance of deep models is now a hot research topic for deep convolutional neural networks. The first choice is to reduce the complexity of the deep CNN models. There have been a few approaches proposed such as Dropout [8] and batch normalization [9].Another choice to improve generalization performance of deep models turns out to be providing sufficient training samples, which are usually hard to satisfy. In most cases, data enhancement skills are adopted to generate more training samples than existing data by adopting various simple operations, such as translation, rotation, flipping, and cropping. Random flipping and cropping are widely used for their good performance in training of deep CNNs, and there have also been some new works for data enhancement, such as mixup [10], RICAP [11], RandomErasing [12], and cutMix [13].In this work, we propose a novel two-stage CNN image classification network, PLANET, which uses a novel image data enhancement method called InnerMove to enhance training samples to obtain more effective discriminating features for image classification.All in all, the main contributions of PLANET are as follows:(i)
Due to hardware barriers, it builds an improved two-stage CNN network for high-resolution image classification. For deep neural networks, a larger input image means a larger model, many more parameters, and more training samples required. The first stage extracts semantic features for a single patch of the image; the second-stage network fuses the semantic features of all patches of the image to classify the image.(ii)
It proposes a novel image enhancement method named InnerMove. InnerMove enhances an image for classification by randomly exchanging the positions of two patches in an image. Position and size of the image patch are randomly generated under certain constraints. It is worth noting that InnerMove can also be used to augment a dataset by applying InnerMove to it multiple times. Augmentation of training samples can inevitably improve the generalization of CNN networks. However, due to hardware barriers, InnerMove is used only once on any input image for data enhancement and augmentation.


## 2. Related Works

InnerMove is proposed mainly for image data enhancement which can be easily adopted in deep CNNs for image classification tasks, which also acts as a regularization approach to improve generalization performance of deep CNNs. In this section, we demonstrate a few recent works related to deep network regularization and image enhancement.


### 2.1. Deep Neural Network Regularization

Regularization plays a fundamental role in preventing deep neural networks from overfitting, which makes neural networks perform well on training data but poor on test data. There have been various deep neural network regularization approaches in the last few years [8, 14–17].Dropout [8] adopts strategy of randomly dropping neurons and corresponding connections during the training process, and experiment results demonstrate that Dropout can significantly improve the performance of deep CNNs on supervised learning tasks in computer vision, speech recognition, document classification, and so on. Then, a generalized version of Dropout named DropConnect [14] was proposed for regularizing large fully connected layers within deep neural networks by randomly setting a subset of activations to zeros. Adaptive Dropout [16] chooses target activations with a probability from a binary belief network, while Stochastic Pooling [17] from a multinomial distribution.


### 2.2. Image Data Enhancement

Data enhancement can be used to enhance existing data to obtain more discriminating features, and in most cases for deep neural network training, data enhancement is often adopted to generate much larger number of data than the existing data for training parameters in deep neural networks. Therefore, data enhancement can also be considered as a form of regularization which achieves regularization target not by adjusting the architecture of the deep neural network but by generating more effective input data [5, 10–13, 18].There are many basic operations for data enhancement such as rotation, flipping, translation, and random cropping [19]. Mixup [10] blends two randomly chosen images and their labels to regularize deep convolutional neural networks. RandomErasing [12] randomly chooses a rectangle region of an image and replaces the corresponding pixels with random values or mean value of images from ImageNet, while Cutout [18] masks out square regions of input images during training.Both Cutmix [13] and RICAP [11] reconstruct new images by cropping and pasting patches within mini-batches with label smoothing; however, there is difference between them. Cutmix crops and switches patches between two images and adjusts their labels with regard to the area of the patches, while RICAP crops four patches from four different images to reconstruct a new image.


## 2.1. Deep Neural Network Regularization

Regularization plays a fundamental role in preventing deep neural networks from overfitting, which makes neural networks perform well on training data but poor on test data. There have been various deep neural network regularization approaches in the last few years [8, 14–17].Dropout [8] adopts strategy of randomly dropping neurons and corresponding connections during the training process, and experiment results demonstrate that Dropout can significantly improve the performance of deep CNNs on supervised learning tasks in computer vision, speech recognition, document classification, and so on. Then, a generalized version of Dropout named DropConnect [14] was proposed for regularizing large fully connected layers within deep neural networks by randomly setting a subset of activations to zeros. Adaptive Dropout [16] chooses target activations with a probability from a binary belief network, while Stochastic Pooling [17] from a multinomial distribution.


## 2.2. Image Data Enhancement

Data enhancement can be used to enhance existing data to obtain more discriminating features, and in most cases for deep neural network training, data enhancement is often adopted to generate much larger number of data than the existing data for training parameters in deep neural networks. Therefore, data enhancement can also be considered as a form of regularization which achieves regularization target not by adjusting the architecture of the deep neural network but by generating more effective input data [5, 10–13, 18].There are many basic operations for data enhancement such as rotation, flipping, translation, and random cropping [19]. Mixup [10] blends two randomly chosen images and their labels to regularize deep convolutional neural networks. RandomErasing [12] randomly chooses a rectangle region of an image and replaces the corresponding pixels with random values or mean value of images from ImageNet, while Cutout [18] masks out square regions of input images during training.Both Cutmix [13] and RICAP [11] reconstruct new images by cropping and pasting patches within mini-batches with label smoothing; however, there is difference between them. Cutmix crops and switches patches between two images and adjusts their labels with regard to the area of the patches, while RICAP crops four patches from four different images to reconstruct a new image.


## 3. Improved Convolutional Neural Networks with Image Enhancement

In this section, we present the details of the proposed CNN network PLANET and image enhancement approach InnerMove.


### 3.1. Details of PLANET

Due to the hardware barriers, we propose an improved two-stage convolutional neural network for high-resolution image classification with a newly proposed image enhancement approach named PLANET. It follows [20] to create many more training samples, and the flowchart of PLANET is illustrated in Figure 1.Figure 1
Flowchart of the proposed improved convolutional neural networks with image enhancement for high-resolution image classification (PLANET). Without loss of generality, we assume that the resolution of training images is2048∗1536. For any resolution, the value of stride for patch extraction is 256 and 512 for the patch-wise and image-wise network, respectively.PLANET first extracts fixed-size patches from an input image by sliding a window of sizeK×K on the image and taking S as the stride. This gives us a total of 1+W−K/S×1+H−K/S patches, where W and H are the width and height of the image, respectively. In order to illustrate the details of PLANET, without loss of generality, we assume that the resolution (W,H) of training images is (2048, 1536). In patch-wise experiment, we choose patches with K = 512 and S=256, so 7×5=35 overlapping image patches are produced, and then each patch will be further enhanced by InnerMove which is detailed in Section 3.2.PLANET adopts GoogLeNet-based patch-wise CNN for semantic feature extraction from patches. Note that the labels for individual patches are unknown, so we use cross-entropy loss based on the corresponding image label to train the patch-wise CNN. Precondition for doing so is that the distribution of effective classification features of the image is relatively uniform, and most patches contain features related to the image label. Experiment results confirm that our strategy is effective and feasible.PLANET designs an image-wise CNN for image classification by fusing all patch semantic features, and it follows the architecture of [20] demonstrated in Figure 2 and makes detailed adjustments. It uses a series of 3 × 3 convolutional layers followed by a 2 × 2 convolutional layer. The pooling layer uses the stride of 2. Each layer has a batch normalization and ReLu activation function. The last convolutional layer is followed by 3 fully connected layers and finally a softmax classifier. To improve the generalization ability of this network, we use Dropout to regularize the network at a rate of 0.5, and use early stopping to limit overfitting when verification accuracy does not improve.Figure 2
Architecture of image-wise CNN. It follows the image-wise CNN in [20] and makes detailed adjustments.In order to train the image-wise network, we no longer extract overlapping patches from images by settingS=512, so the total number of patches extracted from an image is P=12. We use GoogLeNet [21] as our backbone of the patch-wise network, and for each patch, the layer we extract feature maps from for the image-wise classification network has 16 channels of 15×15 feature maps. These 16 channels of 15×15 feature maps extracted from the very end level of GoogLeNet are reconstructed into one channel of 60×60 feature maps. The feature maps extracted from the patch-wise CNN for all 12 patches are then stacked together as a smaller 3D output with the channel size of 12. The image-wise network also uses cross-entropy loss to train its parameters and learns to classify images based on local features generated from the image patch and global information shared among patches.


### 3.2. InnerMove

InnerMove is motivated by the scene of object relative moving in many computer vision tasks such as object tracing, detecting, and recognition. By randomly switching positions of two patches within an input image, InnerMove enhances images for the object moving scene and forces deep neural networks to take the whole scene context into consideration rather than certain local vision features. Image samples enhanced by InnerMove are demonstrated in Figure3.Figure 3
Samples of InnerMove. Four input images of four classes chosen from ICIAR (BACH) 2018 [22] are listed on the left; images on the right side are enhanced samples by InnerMove. The patches chosen to move are noted with small boxes.In our strategy for data enhancement, InnerMove first randomly crops two patches from an input image and then switches their positions. The enhanced image has the same size and label with the original image.In this work, when we extract a patch, it may not contain a whole object because the position and size of patches are chosen randomly. But, it can still force networks to learn more context information of input images and improve the generalization of deep CNNs.


#### 3.2.1. Details of InnerMove

In machine learning, a test set is used to validate the performance of a proposed model by measuring some metrics (M), but we cannot optimize learning models on the test set. Therefore, learning models are usually indirectly optimized on the training set by reducing a certain cost function, noted by Jθ which is defined as (1), during the training process.(1)Jθ=x,y∼p^trainLfx;θ,y,where L is the lost function for training samples, fx;θ is the model which maps x to the prediction y, p^train is one kind of empirical distribution of training data, and θ is the parameter of the learning model f. Ideally, we should get the actual distribution of the data p^data and then calculate the cost function which is defined as (2) for all the data under that distribution.(2)J∗θ=x,y∼p^dataLfx;θ,y,where p^data is the actual distribution of the data.The target of machine learning approach is to reduce the value of (2), which is widely known as risk. If p^data is given, fx;θ will be well trained by optimizing (2). However, in most cases, p^data is unknown.Therefore, sometimes, the optimization of (2) could be simplified into (3) by replacing p^data with p^train:(3)x,y∼p^trainLfx;θ,y=1n∑i=1nLfxi;θ,yi,where p^train gives every single training sample with the same probability and n is the number of training samples.The inability to know the true distribution of the data makes our training data limited in number and content, and data enhancement approaches can be adopted to augment training samples for unknown data distribution to some extent.The partial contribution of this paper is to propose a method (4) to improve the distribution of training data by enhancing training data based on existing data.(4)p^train=μx˜,y˜xi,yi,where x˜,y˜=InnerMovexi,yi, and InnerMove will be detailed soon.So, (1) can be optimized as(5)x˜,y˜∼p^trainLfx˜;θ,y˜=1n∑i=1nLfx˜i;θ,y˜i.In order to optimize (5), gradients of (5) to θ should be calculated as (6) for m training samples in a mini-batch, with which SGD is utilized to update θ.(6)g^=1m∇θ∑i=1mLfx˜i;θ,y˜i.InnerMove randomly chooses two patches without overlapping areas and switches their positions as illustrated in Figure4. Box=p,w,h represents a box which is used to crop a patch within an image. When any input image Io with resolution wo,ho comes, two boxes, b1=p1,w,h and b2=p2,w,h, with two random position points p1, p2, width w, and height h of the patches are generated within the image. In detail, w and h are generated by w⟵intr∗wo and height h⟵intr∗ho, while r∈0,1 is obtained from beta distribution as (7). We choose beta distribution for the reason that it can generate r∈0,1, while restrict r to a narrow scope we desire with a high probability.(7)betaα,b=θa−11−θb−1Bα,b∝θa−11−θb−1,where Bα,b is used to ensure that the integral of betaα,b is equal to 1.Figure 4
Illustration of InnerMove. It randomly chooses two patches without overlapping areas and switches their positions.We need to guarantee that the generated boxes,b1=p1,w,h and b2=p2,w,h, are totally contained within the image, or we will try this process again. It is worth noting that, in this work, b1∩b2 is ϕ. Then, two patches, P1,P2, are cropped from the image Io by performing operations: P1⟵patchExtrab1,Io; P2⟵patchExtrab2,Io. The last step we need to do is pasting these two patches to the chosen positions as pasteP1,p2,Io,pasteP2,p1,Io.


#### 3.2.2. Comparison with Cutout

Cutout [18] is motivated by the scene of object occlusion in image processing, and it adopts the strategy of randomly masking out the square region of the input image to simulate the object occlusion scene. However, InnerMove tries to take the scene of “object moving” into consideration. Both Cutout and InnerMove manage to force deep CNN to take a wider range of features into consideration by cropping chosen patches away from their original positions as shown in Figure 5, and the difference is that Cutout drops chosen patches while InnerMove moves them to other positions within images so that no extra information is lost.Figure 5
Difference between InnerMove and Cutout. Two images are placed above the line, and the corresponding enhanced images from InnerMove and Cutout are listed under the line. They both manage to force deep CNN to take a wider range of features into consideration by cropping chosen patches away from their original positions.


## 3.1. Details of PLANET

Due to the hardware barriers, we propose an improved two-stage convolutional neural network for high-resolution image classification with a newly proposed image enhancement approach named PLANET. It follows [20] to create many more training samples, and the flowchart of PLANET is illustrated in Figure 1.Figure 1
Flowchart of the proposed improved convolutional neural networks with image enhancement for high-resolution image classification (PLANET). Without loss of generality, we assume that the resolution of training images is2048∗1536. For any resolution, the value of stride for patch extraction is 256 and 512 for the patch-wise and image-wise network, respectively.PLANET first extracts fixed-size patches from an input image by sliding a window of sizeK×K on the image and taking S as the stride. This gives us a total of 1+W−K/S×1+H−K/S patches, where W and H are the width and height of the image, respectively. In order to illustrate the details of PLANET, without loss of generality, we assume that the resolution (W,H) of training images is (2048, 1536). In patch-wise experiment, we choose patches with K = 512 and S=256, so 7×5=35 overlapping image patches are produced, and then each patch will be further enhanced by InnerMove which is detailed in Section 3.2.PLANET adopts GoogLeNet-based patch-wise CNN for semantic feature extraction from patches. Note that the labels for individual patches are unknown, so we use cross-entropy loss based on the corresponding image label to train the patch-wise CNN. Precondition for doing so is that the distribution of effective classification features of the image is relatively uniform, and most patches contain features related to the image label. Experiment results confirm that our strategy is effective and feasible.PLANET designs an image-wise CNN for image classification by fusing all patch semantic features, and it follows the architecture of [20] demonstrated in Figure 2 and makes detailed adjustments. It uses a series of 3 × 3 convolutional layers followed by a 2 × 2 convolutional layer. The pooling layer uses the stride of 2. Each layer has a batch normalization and ReLu activation function. The last convolutional layer is followed by 3 fully connected layers and finally a softmax classifier. To improve the generalization ability of this network, we use Dropout to regularize the network at a rate of 0.5, and use early stopping to limit overfitting when verification accuracy does not improve.Figure 2
Architecture of image-wise CNN. It follows the image-wise CNN in [20] and makes detailed adjustments.In order to train the image-wise network, we no longer extract overlapping patches from images by settingS=512, so the total number of patches extracted from an image is P=12. We use GoogLeNet [21] as our backbone of the patch-wise network, and for each patch, the layer we extract feature maps from for the image-wise classification network has 16 channels of 15×15 feature maps. These 16 channels of 15×15 feature maps extracted from the very end level of GoogLeNet are reconstructed into one channel of 60×60 feature maps. The feature maps extracted from the patch-wise CNN for all 12 patches are then stacked together as a smaller 3D output with the channel size of 12. The image-wise network also uses cross-entropy loss to train its parameters and learns to classify images based on local features generated from the image patch and global information shared among patches.


## 3.2. InnerMove

InnerMove is motivated by the scene of object relative moving in many computer vision tasks such as object tracing, detecting, and recognition. By randomly switching positions of two patches within an input image, InnerMove enhances images for the object moving scene and forces deep neural networks to take the whole scene context into consideration rather than certain local vision features. Image samples enhanced by InnerMove are demonstrated in Figure3.Figure 3
Samples of InnerMove. Four input images of four classes chosen from ICIAR (BACH) 2018 [22] are listed on the left; images on the right side are enhanced samples by InnerMove. The patches chosen to move are noted with small boxes.In our strategy for data enhancement, InnerMove first randomly crops two patches from an input image and then switches their positions. The enhanced image has the same size and label with the original image.In this work, when we extract a patch, it may not contain a whole object because the position and size of patches are chosen randomly. But, it can still force networks to learn more context information of input images and improve the generalization of deep CNNs.


### 3.2.1. Details of InnerMove

In machine learning, a test set is used to validate the performance of a proposed model by measuring some metrics (M), but we cannot optimize learning models on the test set. Therefore, learning models are usually indirectly optimized on the training set by reducing a certain cost function, noted by Jθ which is defined as (1), during the training process.(1)Jθ=x,y∼p^trainLfx;θ,y,where L is the lost function for training samples, fx;θ is the model which maps x to the prediction y, p^train is one kind of empirical distribution of training data, and θ is the parameter of the learning model f. Ideally, we should get the actual distribution of the data p^data and then calculate the cost function which is defined as (2) for all the data under that distribution.(2)J∗θ=x,y∼p^dataLfx;θ,y,where p^data is the actual distribution of the data.The target of machine learning approach is to reduce the value of (2), which is widely known as risk. If p^data is given, fx;θ will be well trained by optimizing (2). However, in most cases, p^data is unknown.Therefore, sometimes, the optimization of (2) could be simplified into (3) by replacing p^data with p^train:(3)x,y∼p^trainLfx;θ,y=1n∑i=1nLfxi;θ,yi,where p^train gives every single training sample with the same probability and n is the number of training samples.The inability to know the true distribution of the data makes our training data limited in number and content, and data enhancement approaches can be adopted to augment training samples for unknown data distribution to some extent.The partial contribution of this paper is to propose a method (4) to improve the distribution of training data by enhancing training data based on existing data.(4)p^train=μx˜,y˜xi,yi,where x˜,y˜=InnerMovexi,yi, and InnerMove will be detailed soon.So, (1) can be optimized as(5)x˜,y˜∼p^trainLfx˜;θ,y˜=1n∑i=1nLfx˜i;θ,y˜i.In order to optimize (5), gradients of (5) to θ should be calculated as (6) for m training samples in a mini-batch, with which SGD is utilized to update θ.(6)g^=1m∇θ∑i=1mLfx˜i;θ,y˜i.InnerMove randomly chooses two patches without overlapping areas and switches their positions as illustrated in Figure4. Box=p,w,h represents a box which is used to crop a patch within an image. When any input image Io with resolution wo,ho comes, two boxes, b1=p1,w,h and b2=p2,w,h, with two random position points p1, p2, width w, and height h of the patches are generated within the image. In detail, w and h are generated by w⟵intr∗wo and height h⟵intr∗ho, while r∈0,1 is obtained from beta distribution as (7). We choose beta distribution for the reason that it can generate r∈0,1, while restrict r to a narrow scope we desire with a high probability.(7)betaα,b=θa−11−θb−1Bα,b∝θa−11−θb−1,where Bα,b is used to ensure that the integral of betaα,b is equal to 1.Figure 4
Illustration of InnerMove. It randomly chooses two patches without overlapping areas and switches their positions.We need to guarantee that the generated boxes,b1=p1,w,h and b2=p2,w,h, are totally contained within the image, or we will try this process again. It is worth noting that, in this work, b1∩b2 is ϕ. Then, two patches, P1,P2, are cropped from the image Io by performing operations: P1⟵patchExtrab1,Io; P2⟵patchExtrab2,Io. The last step we need to do is pasting these two patches to the chosen positions as pasteP1,p2,Io,pasteP2,p1,Io.


### 3.2.2. Comparison with Cutout

Cutout [18] is motivated by the scene of object occlusion in image processing, and it adopts the strategy of randomly masking out the square region of the input image to simulate the object occlusion scene. However, InnerMove tries to take the scene of “object moving” into consideration. Both Cutout and InnerMove manage to force deep CNN to take a wider range of features into consideration by cropping chosen patches away from their original positions as shown in Figure 5, and the difference is that Cutout drops chosen patches while InnerMove moves them to other positions within images so that no extra information is lost.Figure 5
Difference between InnerMove and Cutout. Two images are placed above the line, and the corresponding enhanced images from InnerMove and Cutout are listed under the line. They both manage to force deep CNN to take a wider range of features into consideration by cropping chosen patches away from their original positions.


## 3.2.1. Details of InnerMove

In machine learning, a test set is used to validate the performance of a proposed model by measuring some metrics (M), but we cannot optimize learning models on the test set. Therefore, learning models are usually indirectly optimized on the training set by reducing a certain cost function, noted by Jθ which is defined as (1), during the training process.(1)Jθ=x,y∼p^trainLfx;θ,y,where L is the lost function for training samples, fx;θ is the model which maps x to the prediction y, p^train is one kind of empirical distribution of training data, and θ is the parameter of the learning model f. Ideally, we should get the actual distribution of the data p^data and then calculate the cost function which is defined as (2) for all the data under that distribution.(2)J∗θ=x,y∼p^dataLfx;θ,y,where p^data is the actual distribution of the data.The target of machine learning approach is to reduce the value of (2), which is widely known as risk. If p^data is given, fx;θ will be well trained by optimizing (2). However, in most cases, p^data is unknown.Therefore, sometimes, the optimization of (2) could be simplified into (3) by replacing p^data with p^train:(3)x,y∼p^trainLfx;θ,y=1n∑i=1nLfxi;θ,yi,where p^train gives every single training sample with the same probability and n is the number of training samples.The inability to know the true distribution of the data makes our training data limited in number and content, and data enhancement approaches can be adopted to augment training samples for unknown data distribution to some extent.The partial contribution of this paper is to propose a method (4) to improve the distribution of training data by enhancing training data based on existing data.(4)p^train=μx˜,y˜xi,yi,where x˜,y˜=InnerMovexi,yi, and InnerMove will be detailed soon.So, (1) can be optimized as(5)x˜,y˜∼p^trainLfx˜;θ,y˜=1n∑i=1nLfx˜i;θ,y˜i.In order to optimize (5), gradients of (5) to θ should be calculated as (6) for m training samples in a mini-batch, with which SGD is utilized to update θ.(6)g^=1m∇θ∑i=1mLfx˜i;θ,y˜i.InnerMove randomly chooses two patches without overlapping areas and switches their positions as illustrated in Figure4. Box=p,w,h represents a box which is used to crop a patch within an image. When any input image Io with resolution wo,ho comes, two boxes, b1=p1,w,h and b2=p2,w,h, with two random position points p1, p2, width w, and height h of the patches are generated within the image. In detail, w and h are generated by w⟵intr∗wo and height h⟵intr∗ho, while r∈0,1 is obtained from beta distribution as (7). We choose beta distribution for the reason that it can generate r∈0,1, while restrict r to a narrow scope we desire with a high probability.(7)betaα,b=θa−11−θb−1Bα,b∝θa−11−θb−1,where Bα,b is used to ensure that the integral of betaα,b is equal to 1.Figure 4
Illustration of InnerMove. It randomly chooses two patches without overlapping areas and switches their positions.We need to guarantee that the generated boxes,b1=p1,w,h and b2=p2,w,h, are totally contained within the image, or we will try this process again. It is worth noting that, in this work, b1∩b2 is ϕ. Then, two patches, P1,P2, are cropped from the image Io by performing operations: P1⟵patchExtrab1,Io; P2⟵patchExtrab2,Io. The last step we need to do is pasting these two patches to the chosen positions as pasteP1,p2,Io,pasteP2,p1,Io.


## 3.2.2. Comparison with Cutout

Cutout [18] is motivated by the scene of object occlusion in image processing, and it adopts the strategy of randomly masking out the square region of the input image to simulate the object occlusion scene. However, InnerMove tries to take the scene of “object moving” into consideration. Both Cutout and InnerMove manage to force deep CNN to take a wider range of features into consideration by cropping chosen patches away from their original positions as shown in Figure 5, and the difference is that Cutout drops chosen patches while InnerMove moves them to other positions within images so that no extra information is lost.Figure 5
Difference between InnerMove and Cutout. Two images are placed above the line, and the corresponding enhanced images from InnerMove and Cutout are listed under the line. They both manage to force deep CNN to take a wider range of features into consideration by cropping chosen patches away from their original positions.


## 4. Experiment Results

### 4.1. Experiment Setting

In this section, we evaluate the proposed PLANET mainly on image classification tasks and three popular data sets: CIFAR-10 [23], CIFAR-100 [23], and ICIAR (BACH) 2018 [22]. We conduct our experiments on a single NVIDIA Titan XP GPU.There are 4 classes in ICIAR (BACH) 2018 as illustrated in Figure6: normal, benign, inSitu and invasive, and each class has 100 training samples. We adopt 3-fold cross-validation and choose 80 images of each class for training and 20 for validation.Figure 6
Samples of ICIAR (BACH) 2018. There are 4 classes in ICIAR (BACH) 2018: normal, benign, inSitu, and invasive, and each class has 100 training samples. We use 80 of each class for training and 20 for validation.
(a)
(b)
(c)
(d)Due to the serious shortage of training data, this article utilizes the methods adopted by [20] to simply transform each image patch to generate more training samples. Transformation methods include rotations, mirroring, and random color perturbations. By default, the experiments carried out in this article all use patches augmented by the above transformation methods as training samples, which have the same labels as the corresponding images.


### 4.2. Results for InnerMove

It is worth noting that InnerMove can be easily extended by changing the number of patches to switch positions. In this work, InnerMove-2 indicates that the number of patches is 2. Images in CIFAR-10 and CIFAR-100 are small, and the image resolution is32×32, much smaller than 2048×1536 of ICIAR (BACH) 2018. We conduct TSCNN [20] and Resnet18 [24] experiments using InnerMove-2 and InnerMove-4, respectively, for different databases to demonstrate the generalization of InnerMove.


#### 4.2.1. Parameter Selection for InnerMove

For both InnerMove-2 and InnerMove-4, we conduct experiments to evaluate the impact of side length ratio of patches to the original image. As illustrated in Figure7(a), TSCNN with InnerMove-2 performs better on ICIAR (BACH) 2018 with the patch side length ratio ranging from 0.28 to 0.35; results of Resnet18 with InnerMove-4 on CIFAR-10 are demonstrated in Figure 7(b). We can see that the best side length ratio differs with regard to different data sets; therefore, we assign different side length ratio ranges to (r_min, r_max). For CIFAR-10 and CIFAR-100, we adopt (0.35, 0.45). It is worth noting that results of InnerMove depend not only on the side length ratio but also the positions of chosen patches, so the results might slightly vary for different trials.Figure 7
Average accuracy of 2 approaches with InnerMove for the same training and validation data with regard to different side length ratios of the chosen patch to the original image.
(a)
(b)When side length ratio range is given, we verify the effectiveness of InnerMove using TSCNN as the baseline algorithm. Average experiment accuracies of TSCNN with about half pretrained parameters reused on all classes of images in ICIAR (BACH) 2018 within 30 training epochs are illustrated in Figure8. Without loss of generality, we use InnerMove-2 for image enhancement. As demonstrated in Figure 8, in all epochs, accuracies of TSCNN with InnerMove are higher than those without InnerMove when parameters have been well trained.Figure 8
Average accuracy of TSCNN (baseline in the figure) and TSCNN with InnerMove-2 (InnerMove in the figure) on all classes of images in ICIAR (BACH) 2018 in 30 epochs for 3-fold cross-validation.


#### 4.2.2. Comparison with Cutout and RandomErasing

We verify the effectiveness of InnerMove-4 by conducting experiments on CIFAR-10 and CIFAR-100, using Resnet18 as the baseline deep CNN model, and configure the learning model as suggested in [18]. It is worth noting that InnerMove-4 is different from InnerMove-2 in choosing patches from each image: (1) the original image is divided into four blocks of the same size without intersection; (2) InnerMove-4 chooses 4 patches randomly from four blocks with side length ratio from 0.35 to 0.45 which is generated from (7).The experiment comparison between InnerMove, Cutout, and RandomErasing is illustrated in Table1. As shown in Table 1, Resnet18 with InnerMove, Cutout, and RandomErasing performs better than Resnet18 itself, but InnerMove works better than Cutout and RandomErasing when Resnet18 trains on CIFAR-10 and CIFAR-100 with or without other data enhancement methods such as color jitter and brightness adjustment. In detail, InnerMove has significantly improved the accuracy of Resnet18 on C10, C10+, C100, and C100+ by 1.44%, 1.07%, 3.42%, and 0.96%, respectively.Table 1
Test error rates (%) of InnerMove, Cutout, and RandomErasing on CIFAR-10 and CIFAR-100 and C10 and C100 for brevity are illustrated in this table. “+” means training data are augmented by color jitter and brightness adjustment. It is worth noting that the best results are marked in bold.
Method
C10
C10+
C100
C100+
Resnet18 [24]
10.04
6.67
35.84
26.52
Resnet18+Cutout
9.14
5.91
34.37
26.77
Resnet18+RandomErasing
9.47
5.65
35.2
27.16
Resnet18+InnerMove-4
8.6
5.6
32.42
25.56


### 4.3. Results for PLANET

We conduct comparative experiments on ICIAR (BACH) 2018 [22] between PLANET and the following approaches. To simplify the setup of comparative experiments, PLANET and the compared algorithms use InnerMove-2 to enhance images by default.(i)
TSCNN [20]: TSCNN’s network framework is similar to PLANET, and it also builds a two-stage CNN network. The first-stage network is used to learn the semantic features of image patches, and the second-stage network is used to fuse spatial information of the patches to classify images;(ii)
Resnet [24]: Resnet cleverly uses shortcut connections to solve the problem of model degradation in deep networks so that it can be designed deeper and get higher-level semantic information.(iii)
GoogLeNet [21]: GoogLeNet uses multiple inception modules to build the deep network. In the single inception module, multiple small convolution kernels are utilized to replace convolution operations of a large convolution kernel, which greatly reduce the number of parameters. At the same time, inception can make the network extract more kinds of local semantic features.The best experiment results of PLANET within 30 epochs are demonstrated in Table2. As shown in Table 2, in total of 13 comparative indicator values of PLANET with InnerMove, 7 of them are better than those of PLANET without InnerMove, and accuracy has been improved by 3%, especially for inSitu and invasive classes which are of much more importance for clinical diagnosis. For InSitu and Invasive classes, the results of PLANET with InnerMove are much better than those of PLANET without InnerMove in Precision, Recall, and F1 indicators.Table 2
Experiment results, mean± std (%), for 3-fold cross-validation of PLANET with and without InnerMove on ICIAR (BACH) 2018 [22] are illustrated in this table. Precision, Recall, and Accuracy are abbreviated as P, R, and Acc, respectively. It is worth noting that the better results of all the 13 comparative values are marked in bold.
PLANET without InnerMove
PLANET with InnerMove
Class
P
R
F1
Acc
P
R
F1
Acc
Normal
95
±
3
.51
95
±
3
.66
95
±
3
92
92
±
2.89
93
±
2.89
93
±
2.52
95
Benign
94
±
3
.81
97
±
2
.89
95
±
3
.46
89
±
2.31
92
±
2.89
90
±
0.0
InSitu
86
±
3.51
93
±
2.89
90
±
2.89
100
97
±
2
.89
98
±
1
.73
Invasive
94
±
0.0
83
±
2.89
88
±
1.73
$
100
±
0
.0
$
98
±
2
.89
99
±
1
.73The best experiment results of TSCNN within 30 epochs are demonstrated in Table3. As shown in Table 3, (1) the overall performance of the classification algorithm is improved after using InnerMove as image enhancement approach, and accuracy has been improved by 4%; (2) in total of 26 comparative indicator values of TSCNN, only 4 of them are better than those of PLANET with InnerMove, especially for InSitu and Invasive classes which are of much more importance for clinical diagnosis. For InSitu and Invasive classes, the results of PLANET with InnerMove are much better than those of TSCNN with or without InnerMove in Precision, Recall, and F1 indicators. Therefore, PLANET with InnerMove outperforms TSCNN even when TSCNN also utilizes InnerMove for image enhancement.Table 3
Experiment results, mean± std (%), for 3-fold cross-validation of TSCNN on ICIAR (BACH) 2018 [22] are illustrated in this table. Precision, Recall, and Accuracy are abbreviated as P, R, and Acc, respectively. Corresponding results better than PLANET with InnerMove are marked in bold.
TSCNN without InnerMove
TSCNN with InnerMove
Class
P
R
F1
Acc
P
R
F1
Acc
Normal
72
±
1.73
1
0
0
±
0
.
0
84
±
1.15
89
88
±
1.73
9
7
±
2
.
8
9
92
±
1.73
93
Benign
1
0
0
±
0
.
0
65
±
0.0
79
±
0.0
91
±
4.62
83
±
5.77
87
±
1.15
InSitu
95
±
4.51
1
0
0
±
0
.
0
98
±
2.52
97
±
2.89
95
±
0.0
96
±
1.15
Invasive
100
±
0.0
92
±
2.89
96
±
1.15
98
±
2.89
96
±
2.31
97
±
1.53The best experiment results of Resnet18 within 30 epochs are demonstrated in Table4. As shown in Table 4, (1) the overall performance of the classification algorithm is improved after using InnerMove as image enhancement approach, and accuracy has been improved by 3%; (2) in total of 26 comparative indicator values of Resnet18, only 3 of them are better than those of PLANET with InnerMove, especially for InSitu and Invasive classes which are of much more importance for clinical diagnosis. For InSitu and Invasive classes, the results of PLANET with InnerMove are much better than those of Resnet18 with or without InnerMove in Precision, Recall, and F1 indicators. Therefore, PLANET with InnerMove outperforms Resnet18 even when Resnet18 also utilizes InnerMove for image enhancement.Table 4
Experiment results, mean± std (%), for 3-fold cross-validation of Resnet18 on ICIAR (BACH) 2018 [22] are illustrated in this table. Precision, Recall, and Accuracy are abbreviated as P, R, and Acc, respectively. Corresponding results better than PLANET with InnerMove are marked in bold.
Resnet18 without InnerMove
Resnet18 with InnerMove
Class
P
R
F1
Acc
P
R
F1
Acc
Normal
82
±
2.31
95
±
0
.0
87
±
1.15
87
85
±
2.08
97
±
2
.89
91
±
2.52
90
Benign
93
±
0
.58
72
±
2.89
81
±
1.73
90
±
5.51
77
±
5.77
83
±
3.06
InSitu
85
±
1.73
93
±
2.89
89
±
1.15
88
±
2.31
93
±
2.89
91
±
1.73
Invasive
93
±
2.89
90
±
0.0
91
±
1.15
98
±
2.89
95
±
5
97
±
1.53The best experiment results of GoogLeNet within 30 epochs are demonstrated in Table5. As shown in Table 5, (1) the overall performance of the classification algorithm is improved after using InnerMove as image enhancement approach, and accuracy has been improved by 2%; (2) in total of 26 comparative indicator values of GoogLeNet, only one of them is better than those of PLANET with InnerMove, especially for InSitu and Invasive classes which are of much more importance for clinical diagnosis. For InSitu and Invasive classes, the results of PLANET with InnerMove are much better than those of GoogLeNet with or without InnerMove in Precision, Recall, and F1 indicators. Therefore, PLANET with InnerMove outperforms GoogLeNet even when GoogLeNet also utilizes InnerMove for image enhancement.Table 5
Experiment results, mean± std (%), for 3-fold cross-validation of GoogLeNet on ICIAR (BACH) 2018 [22] are illustrated in this table. Precision, Recall, and Accuracy are abbreviated as P, R, and Acc, respectively. Corresponding results better than PLANET with InnerMove are marked in bold.
GoogLeNet without InnerMove
GoogLeNet with InnerMove
Class
P
R
F1
Acc
P
R
F1
Acc
Normal
87
±
2.31
93
±
2.89
90
±
0.0
90
89
±
3.46
93
±
2.08
91
±
1.53
92
Benign
90
±
6.93
83
±
5.77
86
±
0.0
93
±
2
.65
87
±
2.65
90
±
0.0
InSitu
90
±
4.51
95
±
0.0
93
±
2.52
92
±
3.79
92
±
3.79
92
±
0.58
Invasive
97
±
2.89
92
±
2.89
94
±
1.73
95
±
2.52
96
±
4.16
95
±
0.58


## 4.1. Experiment Setting

In this section, we evaluate the proposed PLANET mainly on image classification tasks and three popular data sets: CIFAR-10 [23], CIFAR-100 [23], and ICIAR (BACH) 2018 [22]. We conduct our experiments on a single NVIDIA Titan XP GPU.There are 4 classes in ICIAR (BACH) 2018 as illustrated in Figure6: normal, benign, inSitu and invasive, and each class has 100 training samples. We adopt 3-fold cross-validation and choose 80 images of each class for training and 20 for validation.Figure 6
Samples of ICIAR (BACH) 2018. There are 4 classes in ICIAR (BACH) 2018: normal, benign, inSitu, and invasive, and each class has 100 training samples. We use 80 of each class for training and 20 for validation.
(a)
(b)
(c)
(d)Due to the serious shortage of training data, this article utilizes the methods adopted by [20] to simply transform each image patch to generate more training samples. Transformation methods include rotations, mirroring, and random color perturbations. By default, the experiments carried out in this article all use patches augmented by the above transformation methods as training samples, which have the same labels as the corresponding images.


## 4.2. Results for InnerMove

It is worth noting that InnerMove can be easily extended by changing the number of patches to switch positions. In this work, InnerMove-2 indicates that the number of patches is 2. Images in CIFAR-10 and CIFAR-100 are small, and the image resolution is32×32, much smaller than 2048×1536 of ICIAR (BACH) 2018. We conduct TSCNN [20] and Resnet18 [24] experiments using InnerMove-2 and InnerMove-4, respectively, for different databases to demonstrate the generalization of InnerMove.


### 4.2.1. Parameter Selection for InnerMove

For both InnerMove-2 and InnerMove-4, we conduct experiments to evaluate the impact of side length ratio of patches to the original image. As illustrated in Figure7(a), TSCNN with InnerMove-2 performs better on ICIAR (BACH) 2018 with the patch side length ratio ranging from 0.28 to 0.35; results of Resnet18 with InnerMove-4 on CIFAR-10 are demonstrated in Figure 7(b). We can see that the best side length ratio differs with regard to different data sets; therefore, we assign different side length ratio ranges to (r_min, r_max). For CIFAR-10 and CIFAR-100, we adopt (0.35, 0.45). It is worth noting that results of InnerMove depend not only on the side length ratio but also the positions of chosen patches, so the results might slightly vary for different trials.Figure 7
Average accuracy of 2 approaches with InnerMove for the same training and validation data with regard to different side length ratios of the chosen patch to the original image.
(a)
(b)When side length ratio range is given, we verify the effectiveness of InnerMove using TSCNN as the baseline algorithm. Average experiment accuracies of TSCNN with about half pretrained parameters reused on all classes of images in ICIAR (BACH) 2018 within 30 training epochs are illustrated in Figure8. Without loss of generality, we use InnerMove-2 for image enhancement. As demonstrated in Figure 8, in all epochs, accuracies of TSCNN with InnerMove are higher than those without InnerMove when parameters have been well trained.Figure 8
Average accuracy of TSCNN (baseline in the figure) and TSCNN with InnerMove-2 (InnerMove in the figure) on all classes of images in ICIAR (BACH) 2018 in 30 epochs for 3-fold cross-validation.


### 4.2.2. Comparison with Cutout and RandomErasing

We verify the effectiveness of InnerMove-4 by conducting experiments on CIFAR-10 and CIFAR-100, using Resnet18 as the baseline deep CNN model, and configure the learning model as suggested in [18]. It is worth noting that InnerMove-4 is different from InnerMove-2 in choosing patches from each image: (1) the original image is divided into four blocks of the same size without intersection; (2) InnerMove-4 chooses 4 patches randomly from four blocks with side length ratio from 0.35 to 0.45 which is generated from (7).The experiment comparison between InnerMove, Cutout, and RandomErasing is illustrated in Table1. As shown in Table 1, Resnet18 with InnerMove, Cutout, and RandomErasing performs better than Resnet18 itself, but InnerMove works better than Cutout and RandomErasing when Resnet18 trains on CIFAR-10 and CIFAR-100 with or without other data enhancement methods such as color jitter and brightness adjustment. In detail, InnerMove has significantly improved the accuracy of Resnet18 on C10, C10+, C100, and C100+ by 1.44%, 1.07%, 3.42%, and 0.96%, respectively.Table 1
Test error rates (%) of InnerMove, Cutout, and RandomErasing on CIFAR-10 and CIFAR-100 and C10 and C100 for brevity are illustrated in this table. “+” means training data are augmented by color jitter and brightness adjustment. It is worth noting that the best results are marked in bold.
Method
C10
C10+
C100
C100+
Resnet18 [24]
10.04
6.67
35.84
26.52
Resnet18+Cutout
9.14
5.91
34.37
26.77
Resnet18+RandomErasing
9.47
5.65
35.2
27.16
Resnet18+InnerMove-4
8.6
5.6
32.42
25.56


## 4.2.1. Parameter Selection for InnerMove

For both InnerMove-2 and InnerMove-4, we conduct experiments to evaluate the impact of side length ratio of patches to the original image. As illustrated in Figure7(a), TSCNN with InnerMove-2 performs better on ICIAR (BACH) 2018 with the patch side length ratio ranging from 0.28 to 0.35; results of Resnet18 with InnerMove-4 on CIFAR-10 are demonstrated in Figure 7(b). We can see that the best side length ratio differs with regard to different data sets; therefore, we assign different side length ratio ranges to (r_min, r_max). For CIFAR-10 and CIFAR-100, we adopt (0.35, 0.45). It is worth noting that results of InnerMove depend not only on the side length ratio but also the positions of chosen patches, so the results might slightly vary for different trials.Figure 7
Average accuracy of 2 approaches with InnerMove for the same training and validation data with regard to different side length ratios of the chosen patch to the original image.
(a)
(b)When side length ratio range is given, we verify the effectiveness of InnerMove using TSCNN as the baseline algorithm. Average experiment accuracies of TSCNN with about half pretrained parameters reused on all classes of images in ICIAR (BACH) 2018 within 30 training epochs are illustrated in Figure8. Without loss of generality, we use InnerMove-2 for image enhancement. As demonstrated in Figure 8, in all epochs, accuracies of TSCNN with InnerMove are higher than those without InnerMove when parameters have been well trained.Figure 8
Average accuracy of TSCNN (baseline in the figure) and TSCNN with InnerMove-2 (InnerMove in the figure) on all classes of images in ICIAR (BACH) 2018 in 30 epochs for 3-fold cross-validation.


## 4.2.2. Comparison with Cutout and RandomErasing

We verify the effectiveness of InnerMove-4 by conducting experiments on CIFAR-10 and CIFAR-100, using Resnet18 as the baseline deep CNN model, and configure the learning model as suggested in [18]. It is worth noting that InnerMove-4 is different from InnerMove-2 in choosing patches from each image: (1) the original image is divided into four blocks of the same size without intersection; (2) InnerMove-4 chooses 4 patches randomly from four blocks with side length ratio from 0.35 to 0.45 which is generated from (7).The experiment comparison between InnerMove, Cutout, and RandomErasing is illustrated in Table1. As shown in Table 1, Resnet18 with InnerMove, Cutout, and RandomErasing performs better than Resnet18 itself, but InnerMove works better than Cutout and RandomErasing when Resnet18 trains on CIFAR-10 and CIFAR-100 with or without other data enhancement methods such as color jitter and brightness adjustment. In detail, InnerMove has significantly improved the accuracy of Resnet18 on C10, C10+, C100, and C100+ by 1.44%, 1.07%, 3.42%, and 0.96%, respectively.Table 1
Test error rates (%) of InnerMove, Cutout, and RandomErasing on CIFAR-10 and CIFAR-100 and C10 and C100 for brevity are illustrated in this table. “+” means training data are augmented by color jitter and brightness adjustment. It is worth noting that the best results are marked in bold.
Method
C10
C10+
C100
C100+
Resnet18 [24]
10.04
6.67
35.84
26.52
Resnet18+Cutout
9.14
5.91
34.37
26.77
Resnet18+RandomErasing
9.47
5.65
35.2
27.16
Resnet18+InnerMove-4
8.6
5.6
32.42
25.56


## 4.3. Results for PLANET

We conduct comparative experiments on ICIAR (BACH) 2018 [22] between PLANET and the following approaches. To simplify the setup of comparative experiments, PLANET and the compared algorithms use InnerMove-2 to enhance images by default.(i)
TSCNN [20]: TSCNN’s network framework is similar to PLANET, and it also builds a two-stage CNN network. The first-stage network is used to learn the semantic features of image patches, and the second-stage network is used to fuse spatial information of the patches to classify images;(ii)
Resnet [24]: Resnet cleverly uses shortcut connections to solve the problem of model degradation in deep networks so that it can be designed deeper and get higher-level semantic information.(iii)
GoogLeNet [21]: GoogLeNet uses multiple inception modules to build the deep network. In the single inception module, multiple small convolution kernels are utilized to replace convolution operations of a large convolution kernel, which greatly reduce the number of parameters. At the same time, inception can make the network extract more kinds of local semantic features.The best experiment results of PLANET within 30 epochs are demonstrated in Table2. As shown in Table 2, in total of 13 comparative indicator values of PLANET with InnerMove, 7 of them are better than those of PLANET without InnerMove, and accuracy has been improved by 3%, especially for inSitu and invasive classes which are of much more importance for clinical diagnosis. For InSitu and Invasive classes, the results of PLANET with InnerMove are much better than those of PLANET without InnerMove in Precision, Recall, and F1 indicators.Table 2
Experiment results, mean± std (%), for 3-fold cross-validation of PLANET with and without InnerMove on ICIAR (BACH) 2018 [22] are illustrated in this table. Precision, Recall, and Accuracy are abbreviated as P, R, and Acc, respectively. It is worth noting that the better results of all the 13 comparative values are marked in bold.
PLANET without InnerMove
PLANET with InnerMove
Class
P
R
F1
Acc
P
R
F1
Acc
Normal
95
±
3
.51
95
±
3
.66
95
±
3
92
92
±
2.89
93
±
2.89
93
±
2.52
95
Benign
94
±
3
.81
97
±
2
.89
95
±
3
.46
89
±
2.31
92
±
2.89
90
±
0.0
InSitu
86
±
3.51
93
±
2.89
90
±
2.89
100
97
±
2
.89
98
±
1
.73
Invasive
94
±
0.0
83
±
2.89
88
±
1.73
$
100
±
0
.0
$
98
±
2
.89
99
±
1
.73The best experiment results of TSCNN within 30 epochs are demonstrated in Table3. As shown in Table 3, (1) the overall performance of the classification algorithm is improved after using InnerMove as image enhancement approach, and accuracy has been improved by 4%; (2) in total of 26 comparative indicator values of TSCNN, only 4 of them are better than those of PLANET with InnerMove, especially for InSitu and Invasive classes which are of much more importance for clinical diagnosis. For InSitu and Invasive classes, the results of PLANET with InnerMove are much better than those of TSCNN with or without InnerMove in Precision, Recall, and F1 indicators. Therefore, PLANET with InnerMove outperforms TSCNN even when TSCNN also utilizes InnerMove for image enhancement.Table 3
Experiment results, mean± std (%), for 3-fold cross-validation of TSCNN on ICIAR (BACH) 2018 [22] are illustrated in this table. Precision, Recall, and Accuracy are abbreviated as P, R, and Acc, respectively. Corresponding results better than PLANET with InnerMove are marked in bold.
TSCNN without InnerMove
TSCNN with InnerMove
Class
P
R
F1
Acc
P
R
F1
Acc
Normal
72
±
1.73
1
0
0
±
0
.
0
84
±
1.15
89
88
±
1.73
9
7
±
2
.
8
9
92
±
1.73
93
Benign
1
0
0
±
0
.
0
65
±
0.0
79
±
0.0
91
±
4.62
83
±
5.77
87
±
1.15
InSitu
95
±
4.51
1
0
0
±
0
.
0
98
±
2.52
97
±
2.89
95
±
0.0
96
±
1.15
Invasive
100
±
0.0
92
±
2.89
96
±
1.15
98
±
2.89
96
±
2.31
97
±
1.53The best experiment results of Resnet18 within 30 epochs are demonstrated in Table4. As shown in Table 4, (1) the overall performance of the classification algorithm is improved after using InnerMove as image enhancement approach, and accuracy has been improved by 3%; (2) in total of 26 comparative indicator values of Resnet18, only 3 of them are better than those of PLANET with InnerMove, especially for InSitu and Invasive classes which are of much more importance for clinical diagnosis. For InSitu and Invasive classes, the results of PLANET with InnerMove are much better than those of Resnet18 with or without InnerMove in Precision, Recall, and F1 indicators. Therefore, PLANET with InnerMove outperforms Resnet18 even when Resnet18 also utilizes InnerMove for image enhancement.Table 4
Experiment results, mean± std (%), for 3-fold cross-validation of Resnet18 on ICIAR (BACH) 2018 [22] are illustrated in this table. Precision, Recall, and Accuracy are abbreviated as P, R, and Acc, respectively. Corresponding results better than PLANET with InnerMove are marked in bold.
Resnet18 without InnerMove
Resnet18 with InnerMove
Class
P
R
F1
Acc
P
R
F1
Acc
Normal
82
±
2.31
95
±
0
.0
87
±
1.15
87
85
±
2.08
97
±
2
.89
91
±
2.52
90
Benign
93
±
0
.58
72
±
2.89
81
±
1.73
90
±
5.51
77
±
5.77
83
±
3.06
InSitu
85
±
1.73
93
±
2.89
89
±
1.15
88
±
2.31
93
±
2.89
91
±
1.73
Invasive
93
±
2.89
90
±
0.0
91
±
1.15
98
±
2.89
95
±
5
97
±
1.53The best experiment results of GoogLeNet within 30 epochs are demonstrated in Table5. As shown in Table 5, (1) the overall performance of the classification algorithm is improved after using InnerMove as image enhancement approach, and accuracy has been improved by 2%; (2) in total of 26 comparative indicator values of GoogLeNet, only one of them is better than those of PLANET with InnerMove, especially for InSitu and Invasive classes which are of much more importance for clinical diagnosis. For InSitu and Invasive classes, the results of PLANET with InnerMove are much better than those of GoogLeNet with or without InnerMove in Precision, Recall, and F1 indicators. Therefore, PLANET with InnerMove outperforms GoogLeNet even when GoogLeNet also utilizes InnerMove for image enhancement.Table 5
Experiment results, mean± std (%), for 3-fold cross-validation of GoogLeNet on ICIAR (BACH) 2018 [22] are illustrated in this table. Precision, Recall, and Accuracy are abbreviated as P, R, and Acc, respectively. Corresponding results better than PLANET with InnerMove are marked in bold.
GoogLeNet without InnerMove
GoogLeNet with InnerMove
Class
P
R
F1
Acc
P
R
F1
Acc
Normal
87
±
2.31
93
±
2.89
90
±
0.0
90
89
±
3.46
93
±
2.08
91
±
1.53
92
Benign
90
±
6.93
83
±
5.77
86
±
0.0
93
±
2
.65
87
±
2.65
90
±
0.0
InSitu
90
±
4.51
95
±
0.0
93
±
2.52
92
±
3.79
92
±
3.79
92
±
0.58
Invasive
97
±
2.89
92
±
2.89
94
±
1.73
95
±
2.52
96
±
4.16
95
±
0.58


## 5. Conclusion

In this work, we propose an improved two-stage image classification CNN network called PLANET. The first-stage network is used to extract semantic features of image patches, and the second-stage network is used to fuse the semantic features of all patches to classify images. This paper also proposes an image enhancement method called InnerMove, which randomly selects two or more patches and switches their positions within an image to simulate the “object movement” scene of computer vision tasks. Sufficient experiments have been conducted for classification tasks on CIFAR-10, CIFAR-100, and ICIAR (BACH) 2018. Experiment results show that PLANET has better classification performance than comparative algorithms, and InnerMove is effective and feasible for data enhancement in image classification tasks. We plan to further investigate the usage of InnerMove in other computer vision tasks such as image segmentation and object detection.



---

*Source: 1245924-2020-03-11.xml*