Pneumonia Detection from Chest X-Ray Images Using Convolutional Neural Network

The fact that Pneumonia ranks among the world's most common causes of mortality, precise diagnosis methods are absolutely necessary. Although chest X-rays are a quick way to identify Pneumonia, they can be challenging to interpret due to the similarities between Pneumonia and other lung conditions. This research presents a computer-aided approach to Pneumonia diagnosis that uses chest x-rays to enhance diagnostic decision-making. Moreover, situations such as the coronavirus pandemic, where widespread lockdowns are implemented and human contact poses significant risks, highlight the importance of computer-aided diagnosis like this. Consequently, a technique for the quick and automatic diagnosis of Pneumonia is presented in this work. Based on chest X-ray pictures, a deep learning-based architecture called "ESPD" is suggested for the automatic diagnosis of Pneumonia. A benchmark dataset comprising 5,856 chest X-ray pictures was utilized for the suggested deep-learning network's testing, evaluation, and training. The total accuracy of the suggested model was found to be 98.24%, consisting of 0.98 F1-Score, 0.98 precision, 0.98 recall, and 0.97 specificity. In comparison to previous methods in the literature, the suggested method was found to be quicker and less computationally expensive, and it also produced accuracy that showed promise.