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• A deep learning method is proposed using different optimizers and transfer learning to classify Pneumonia patients. • Preparing a dataset of around 5300 X-ray images for pneumonia detection. • The proposed deep transfer learning method is trained on a benchmark open dataset of chest x-ray images. • Presenting the optimization results, precision, recall, accuracy, and F1-score for proposed method. • In proposed method achieve better accuracy in the detection rate than other techniques. Pneumonia is a disease that leads to the death of individuals within a short period since the flow of fluid in the lungs. Hence, initial diagnosis and drugs are very important to avoid the progress of the disease. This paper proposes a novel deep learning approach for automatic detection of pneumonia using deep transfer learning to simplify the detection process with improved accuracy. This work was aimed to preprocess the input chest X-ray images to identify the presence of pneumonia using U-Net architecture based segmentation and classifies the pneumonia as normal and abnormal (Bacteria, viral) using pre-trained on ImageNet dataset models such as ResNet50, InceptionV3, InceptionResNetV2. Besides, to extract the efficient features and improve accuracy of pre-trained models two optimizers, namely, Adam and Stochastic Gradient Descent (SGD) used and its performances are analyzed with batch sizes of 16 and 32. Based on the values obtained, the performances of undertaken pre-trained models are analyzed and compared with other Convolutional Neural Network (CNN) models such as DenseNet-169+SVM, VGG16, RetinaNet + Mask RCNN, VGG16 and Xception, Fully connected RCNN, etc using various measures. From the results observed that the proposed ResNet50 model work achieved 93.06% accuracy, 88.97 % precision rate, 96.78% Recall rate and 92.71% F1-score rate, which than is higher than the other models aforementioned. [ABSTRACT FROM AUTHOR]

• Deep learning-based pneumonia detection in x-ray images is done in this work. • Different models of deep learning and transfer learning are analysed in this work for the image classification application. • An extensive analysis is carried out in this work with several experimental results. A large number of children die due to pneumonia every year worldwide. An estimated 1.2 million episodes of pneumonia were reported in children up to 5 years of age, of which 880,000 died in 2016. Hence, pneumonia is a major cause of death amongst children, with high prevalence rate in South Asia and Sub-Saharan Africa. Even in a developed country like the United States, pneumonia is among the top 10 causes of deaths. Early detection and treatment of pneumonia can reduce mortality rates among children significantly in countries having a high prevalence. Hence, this paper presents Convolutional Neural Network models to detect pneumonia using x-ray images. Several Convolutional Neural Networks were trained to classify x-ray images into two classes viz., pneumonia and non-pneumonia, by changing various parameters, hyperparameters and number of convolutional layers. Six models have been mentioned in the paper. First and second models consist of two and three convolutional layers, respectively. The other four models are pre-trained models, which are VGG16, VGG19, ResNet50, and Inception-v3. The first and second models achieve a validation accuracy of 85.26% and 92.31% respectively. The accuracy of VGG16, VGG19, ResNet50 and Inception-v3 are 87.28%, 88.46%, 77.56% and 70.99% respectively. [ABSTRACT FROM AUTHOR]