# A Novel Transfer Learning Based Approach for Pneumonia Detection in Chest X-ray Images
## 1 Introduction
[19] contributed by proposing a new dataset called ChestX-ray8 that contains images of different 32,717 patients, having 108,948 frontal X-ray images. They achieved promising results using a deep CNN. They further stated that this dataset can be extended using more disease labels. Not only in chest X-rays, [PERSON] et al. [20], used the power of data augmentation and CNN, and achieved great results, even training on small sample of images. In another study, [PERSON] et al. [21] showed how deep CNN can be used to detect lymph nodes. They got promising results even in case of an adverse quality of images. [PERSON] et al. [22] worked on different CNN architectures and addressed the problem of lymph and lung disease detection. [PERSON] et al. [23] suggested CheXNeXt, a very deep CNN with 121 layers, to detect 14 different pathologies, including pneumonia, in frontal-view chest X-rays. First, neural networks were trained to forecast the probability of 14 abnormalities in the X-ray image. Then, an ensemble of those networks, were used to issue predictions by calculating the mean predictions of individual networks. [PERSON] et al. [24] suggested a novel algorithm for training probabilistic neural networks (PNNs) that allows one to construct smaller networks. [PERSON] et al. [25] used a 3D deep CNN and a multiscale forecasting strategy, with cube clustering and multiscale cube prediction, for lung nodule detection. [PERSON] and [PERSON] [26] used a pretrained DenseNet-121 and four types of local features and deep featuresacquired by using the scale-invariant feature transform (SIFT), GIST, Local binary patterns (LBP) and a histogram of oriented gradients (HOGs), and CNN features for classification of 14 thoracic diseases. [PERSON] et al. [27] used Mask-RCNN, a deep neural network, which utilizes both global and local features for pulmonary image segmentation combined with image augmentation, alongside with dropout and L2 regularization, for pneumonia identification. [PERSON] et al. [28] use a 3D deep CNN (3D DCNN) which has shortcut connections and a 3D DCNN with dense connections. The shortcuts and dense connections solve the gradient vanishing problem. Connections allow deep networks to capture both the general and specific features of lung nodules. [PERSON] and [PERSON] [29] use AlexNet and GoogLeNet neural networks with data augmentation and without any pre-training to obtain an Area Under the Curve (AUC) of 0.94-0.95. An improved architecture using transfer learning and a two-network ensemble achieved an AUC of 0.99. [PERSON] et al. [30] used a CNN-based approach combined with rib suppression and lung filed segmentation. For pixel patches acquired in the lung area, three CNNs were trained on different resolution images, and feature fusion was applied to merge all information. [PERSON] et al. [31] designed a novel network with residual structures, which has 49 convolutional layers, only one global average pooling layer and two dense connection layers for pediatric pneumonia diagnosis. [PERSON] et al. [32] used a deep CNN with 25 layers and eight residual connections. The outputs of three networks trained with different hyperparameter values were averaged for the prediction of malignant pulmonary nodules in chest radiographs. [PERSON] et al. [33] used two 3D-customized mixed link network (CMixNet) architectures. Lung nodule recognition was performed with faster R-CNN on features learned from CMixNet and U-Net like encoder-decoder, while a gradient boosting machine (GBM) was used for classification. [PERSON] et al. [34] proposed a custom neural network consisting of five convolutional blocks (each having two \(3\times 3\) convolutions with Rectified Linear Units (ReLUs), followed by a max-pooling operation), a global average pooling layer and a fully-connected softmax layer with two outputs. [PERSON] et al. [35] suggested a 3D fully CNN for the fast screening and generation of candidate suspicious regions.