Pneumonia detection using modified VGG 19 net architecture: application of deep convolutional neural network

Pneumonia is a somewhat common condition that may be brought on by a variety of micro-organisms, including bacteria, viruses, and fungus. The use of a deep convolutional neural network fed with X-ray images to detect lung pneumonia has been proposed. Collecting data, enhancing images, performing an adaptive and precise ROI evaluation, extracting features, and diagnosing diseases are all part of the framework's scope. The suggested deep CNN models were trained using 12,000 pictures from the Pneumonia chest X-ray Dataset, which contained both infected and uninfected chest X-rays. The publicly available new CNN models were trained, and the results were compared with ensemble approach. On the validation set, the suggested technique demonstrated its superiority with an AUC of 95.21 and a sensitivity of 97.76. The proposed technique had a 90.71% success rate in properly identifying images of a patient's chest as normal, viral pneumonia, or bacterial pneumonia.