# A Novel Transfer Learning Based Approach for Pneumonia Detection in Chest X-ray Images
## 1 Introduction
Next, an ensemble of 3-D CNNs was trained using extensive data augmentations obtained from the positive and negative patches. The classifiers were trained on false positive patches using different thresholds and data augmentation types. Finally, the outputs of second stage networks were averaged to produce the final prediction. [PERSON] et al. [36] suggested an ensemble of RetinaNet and Mask R-CNN networks for pneumonia localization. First, networks recognized the pneumonia-affected regions, then non-maximum suppression was applied in the predicted lung regions. [PERSON] et al. [37] used AlexNet-based CNN for lung patch classification. Then, a second CNN model, based on ResNet18, was employed to reconstruct the missing parts of the lung area. The output is obtained by the ensemble combination of initial segmentation and reconstruction. [PERSON] et al. [38] used standard network architectures (VGG16/19, Xception, Inception and ResNet) and explored the parameter space of pooling and flattening operations. Final results were obtained by fully connected layers followed by a sigmoid function. [PERSON] et al. [39] split X-ray images into six types of patches and used ResNet to classify six patches and recognize lung nodules. They used rotation, translation and scaling techniques for data augmentation. [PERSON] et al. [40] proposed a multi-scale heterogeneous 3D CNN (MSH-CNN), which uses multiscale 3D nodule blocks with contextual data as input; (2) two 3D CNNs to acquire expression features; (3) and a set of weights defined by backpropagation for feature fusion. [PERSON] et al. [41] suggested a semi-supervised adversarial classification (SSAC) framework that is trained using both labeled and unlabeled data. They used an adversarial autoencoder-based unsupervised network for reconstruction, a supervised network for classification and transition layers that map from the reconstruction network to the classification network. [PERSON] et al. [42] designed a hierarchical CNN CXNet-m1, which used a novel sin-loss loss function to learn from misclassified and indistinguishable images for anomaly identification on chest X-rays. [PERSON] et al. [43] retrained a final layer of the CNN model, Inception v3, and performed binary normality classification. In addition, [PERSON] et al. [44] compared the ResNet50 feature extractor combined with the SVM RBF classifier for recognition of the malignancy of a lung nodule. [PERSON] et al. [45] used the spatial distribution of Hue, Saturation and Brightness in the X-ray images as image descriptors, and an ANN with heuristic algorithms (Moth-Flame and Ant Lion optimization) to detect diseased lung tissues. Finally, [PERSON] et al. [46] used transfer learning with DCNNs pretrained on the ImageNet dataset and combined with a problem-based ChestNet architecture with unnecessary pooling layers removed, and a three-step pre-processing to support model generalization. [PERSON] et al. [9] systematically investigated brain signal types and related deep learning concepts for brain signal analysis, and they covered open challenges and future directions for brain signal analysis with help of 230 contributions performed by researchers. [PERSON] et al. [5] proposed three-stage deep features learning and a fusion framework for identifying Alzheimer disease and its prodromal status. [PERSON] et al. [47] proposed a fully convolutional network deep learning approach for image registration to predict the deformation field which become insensitive to parameter tuning and small hierarchical loss. [PERSON] et al. [1] exploited the usage of a deep convolutional network to explore the uses of local description and feature encoding for image representation and registration. Apart from these significant achievements, CNNs work very well on large datasets. However, most of the time they fail on small datasets if proper care is not taken.