Computational Intelligence and Healthcare Informatics. Группа авторов

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СКАЧАТЬ and deep learning approaches are used and are compared on the basis of error rate, accuracy, and training time [2]. Conventional models include Back Propagation Neural Network (BPNN) and Competitive Neural Network (CpNN) and deep learning model includes simple CNN. Deep CNN has better generalization ability than BPNN and CpNN but requires more iteration due to extraction of features at different layers.

      A pre-defined CNN for binary classification of chest radiographs which assess their ability on live customized dataset obtained from U.S. National Institutes of Health is presented in [18]. Before applying deep learning models, the dataset is separated into different categories and labeled manually with two different radiologist. Their labels are tallied and conflicting images are discarded. Normal images without any pathology were removed and 200,000 images were finally used for training purpose. Out of those images, models were trained on different number of images and performance of models noted in terms of AUC score. It is observed that modestly size images achieve better accuracy for binary classification into normal and abnormal chest radiograph. This automated image analysis will be useful in poor resource areas.

      The CheXNet deep learning algorithm is used to detect 14 pathologies in chest radio-graphs where the 121-layer DenseNet architecture is densely connected [49]. Ensemble network is generated by allowing multiple network to get trained on training set and networks which has less average prediction error are selected to become the part of ensemble network. The parameters of each ensemble network are initialized using the ImageNet pretrained network. The image input size is 512 × 512 and the optimization of Adams was used to train the NN parameter with batch size of 8 and learning rate of 0.0001. To prevent dropouts and decay, network was saved after every epoch. To deal with overfitting, early stopping of iteration was done.

Considering the severity of TB which is classified as the fifth leading cause of death worldwide, with 10 million new cases and 1.5 million deaths per year, DL models are proposed to detect it from CXR. Being one of the world’s biggest threats and being rather easy to cure, the World Health Organization (WHO) recommends systematic and broad use of screening to extirpate the disease. Posteroanterior chest radiography, in spite its low specificity and difficulty in interpretation, is still unfortunately one of the preferred TB screening methods. Since TB is primarily a disease of poor countries, the clinical officers trained to interpret these CXRs are often rare in number. In such circumstances, an automated algorithm for TB diagnosis could be an inexpensive and effective method to make widespread TB screening a reality. As a consequence, this has attracted the attention of the machine learning community [9, 27, 28, 30, 33, 35, 38, 40 , 42, 68] which has tackled the problem with methods ranging from hand-crafted algorithm to support vector machines and convolutional neural networks. Considering the rank of TB in the list of cause of death worldwide, deep learning models are implemented for fast screening of TB [46]. The results are encouraging, as some of these methods achieve nearly-human sensitivities and specificities. Considering the limitation of availability of powerful and costly hardware and large number learning parameters, a simple Deep CNN model has been proposed for CXR TB screening rather than using complex machine learning pipelining as used in [30, 40, 42, 68]. The saliency maps and the grad-CAMs have been used for the first time to provide better visualization effects. As radiologist is having deeper perspective of the chest abnormalities, this model is helpful in providing second opinion to them. The architecture of model consists of five blocks of convolution followed by global average pooling layer and fully connected softmax layer. In between each convolutional block, a max pooling layer is inserted moreover, the overall arrangement is similar to AlexNet. Batch normalization is used by each convolution layer to avoid problem of overfitting. After training of network, silency-maps and grad-CAM are used for better visualization. Silency-maps help generating heat map with same resolution as input image and grad-CAM helps in better localization with poor resolution due to pooling. NIH Tuberculosis Chest X-ray dataset [29] and Belarus Tuberculosis portal dataset [6] are used for experimentation. It is observed that model facilitates better visualization of presence or absence of TB for clinical practitioners. Subsequently, by considering the severity of Pneumonia, a novel model which is ensemble of two models RetinaNet and Mask R-CNN is proposed in [61] and is tested on Kaggle pneumonia detection competition dataset consisting of 26,684 images. Transfer learning is applied for weight initialization from models trained on Microsoft COCO challenge. To detect the object, RetinaNet is utilized first and then Mask R-CNN is employed as a supplementary model. Both these models are allowed to individually predict pneumonia region. If bounding box of predicted region from both models overlapped then averaged was taken on the basis of weight ratio 3:1, otherwise it was used in the dataset without any change for detection by ensemble model. In addition, Recall score is obtained by the ensemble model is 0.734.

      A model, namely, ChestNet, is proposed for detection of consolidation, a kind of lung opacity in pediatric CXR images [5]. Consolidation is one of the critical abnormalities whose detection helps in early prediction of pneumonia. Before applying model, three-step pre-processing is done to deal with the issues, namely, checking the presence of confounding variables in the image, searching for consolidation patterns instead of using histogram features, and learning is used to detect sharp edges such as ribs and spines instead of directly detecting pattern of consolidation by the CNN. ChestNet models consist of convolutional layers, batch normalization layers embedded after each convolutional layer, and two classifier layers at the last. Only two max-pooling layers were used in contrast to five layers of VGG16, and DenseNet121 in order to preserve the region of image where the consolidation pattern is spread out. Smaller size convolutional layer such as 3 × 3 learns undesirable features, so to avoid this author used 7 × 7 size convolutional layer to learn largely spread consolidation pattern.

A multi-attention framework to deal with issues like class imbalance, shortage of annotated images, and diversity of lesion areas is developed in [41] and ChestX-ray14 dataset is used for experimental purpose. Three modules which are implemented by the authors are feature attention module, space attention module, and hard example attention module. In feature attention module, interdependencies of pathologies are detected considering structure of ResNet101 model as base. Because of the ability of Squeeze and Excitation (SE) block [35] to model channel interdependencies of modules, one SE block is inserted into each ResNet block. The feature map generated by this module contains lots of noise and is learnt from global information rather than concentrating on small diseases related region. To avoid this, space attention module is introduced. In this module, global average pooling is applied on feature map obtained from ResNet101 [39]. This help in carrying out global information of image in each pixel which benefits classification and localization task. In hard attention modules, positive and negative images are separated into two different sets and model is trained on these individual sets to obtain threshold value of predicted score for each set. Then, set C is created which is combination of both sets and contained increased proportions of positive samples. The models is retrained on set C to distinguish 14 classes of thoracic diseases. This helps in resolving issue of presence of large gap in positive and negative samples.

      Multiple feature extraction technique was used by author in paper [23] for the classification of thoracic pathologies. Various classifiers such as Gaussian discriminant analysis (GDA), KNN, Naïve Bayes, SVM, Adaptive Boosting (AdaBoost), Random forest, and ELM were compared with pretrained DenseNet121 which was used for localization by generating CAM (Class Activation Map) and integrated results of different shallow and deep feature extraction algorithms such as Scale Invariant Feature Transform (SIFT), Gradient-based (GIST), Local Binary Pattern (LBP), and Histogram Oriented Gradient–based (HOG) with different classifiers have been used for final classification of various lung abnormalities. It is observed that ELM is having better F1-score than the DenseNet121.

      Two asymmetric networks ResNet and DenseNet which extract complementary unique features from input image were used to design new ensemble model known as DualCheXNet [10]. It has been the first attempt to use complementarity of dual asymmetric subnetworks developed in the field of thoracic disease classification. Two networks, i.e., ResNet and DenseNet are allowed to work simultaneously in Feature Level Fusion (FLF) module and selected features from both networks are combined in Decision Level fusion (DLF) on which two auxiliary classifiers are applied for classifying image into one of the pathologies.

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