Customized VGG19 Architecture for Pneumonia Detection in Chest X-Rays.

• This work proposes a Modified VGG19 deep-learning architecture to diagnose the Chest X-Rays. • This work computes the essential handcrafted features from the Chest X-Rays. • This work introduces an Ensemble of Features Scheme (EFS) by integrating the deep-features and the handcrafted features. • Serial fusion and PCA based selection is implemented in EFS to choose primary feature set. • Experimental results demonstrate superior performance of Modified VGG19 in comparison with AlexNet, VGG16, VGG19 and ResNet50. Pneumonia is one of the major illnesses in children and aged humans due to the Infection in the lungs. Early analysis of pneumonia is necessary to prepare for a possible treatment procedure to regulate and cure the disease. This research aspires to develop a Deep-Learning System (DLS) to diagnose the lung abnormality using chest X-ray (radiograph) images. The proposed work is implemented using; (i) Conventional chest radiographs and (ii) Chest radiograph treated with a threshold filter. The initial experimental evaluation is carried out using the traditional DLS, such as AlexNet, VGG16, VGG19 and ResNet50 with a SoftMax classifier. The results confirmed that, VGG19 provides better classification accuracy (86.97%) compared to other methods. Later, a customized VGG19 network is proposed using the Ensemble Feature Scheme (EFS), which combines the handcrafted features attained with CWT, DWT and GLCM with the Deep-Features (DF) achieved using Transfer-Learning (TL) practice. The performance of customized VGG19 is tested using different classifiers, such as SVM-linear, SVM-RBF, KNN classifier, Random-Forest (RF) and Decision-Tree (DT). The result confirms that VGG19 with RF classifier offers better accuracy (95.70%). When the similar experiment is repeated using threshold filter treated chest radiographs, the VGG19 with RF classifier offered superior classification accuracy (97.94%). This result confirms that, proposed DLS will work well on the benchmark images and in the future, it can be considered to diagnose clinical grade chest radiographs. To create your abstract, type over the instructions in the template box below. Fonts or abstract dimensions should not be changed or altered. Image, graphical abstract [ABSTRACT FROM AUTHOR]