Deep Learning Based Automated Pneumonia Detection from X-ray Images

Artificially Intelligent (AI) based systems possess the capability of successfully realizing the insights from frequently available huge datasets. Various health care and medical imaging applications have been significantly benefited for making accurate decisions for improving the health conditions of the patients. Nowadays, medical science area have witnessed enormous advancement with the effective utilization of AI and deep learning (DL) techniques for various applications namely automated patient monitoring, prescribing medications, in-depth analysis of severe disease patterns and performing laser based surgeries based on the observations of the disease patterns occurring in the medical based digital images. Most of the automated medical oriented digital images are generated using MRIs, X-rays and CT scans which makes the processing of such images by numerous modern AI and DL algorithms efficient and highly accurate for critical decision making and medical treatment diagnosis. Conclusively, human based expert system is replaced by various AI and DL based systems which has fast processing and accurate reporting capabilities especially in medical science domain. Thus, our article proposes the utilization of AI and DL based techniques to efficiently propose diagnosis for patients suffering from pneumonia based on the gathered X-ray images. Precisely, our article focuses upon the effective utilization of DL based modified convolutional neural network (CNN) and visual geometry group (VGG-16) for accurately classifying the chest X-ray images into normal and pneumonia categories. Proposed model based on the recognition of the pattern changes in the chest X-ray images have the ability to classify the disease into either a normal or a pneumonia case with an overall training and validation accuracy of 97% respectively.