Deep Learning with Multi-Class Classification for Detection of Covid-19 and Pneumonia

Early detection of pneumonia disease and COVID-19 can increase the survival rate of patients with lung infections. While the signs and symptoms of COVID-19 and pneumonia are quite similar, a chest X-ray can distinguish between the two to identify and diagnose each condition. A trained radiologist may find it challenging to distinguish between pneumonia and COVID-19 from CXR pictures since manual mistakes are quite likely to occur. The classification of images for use in medical imaging and other fields benefits greatly from deep learning techniques. The problem statement is that it is difficult to distinguish COVID-19 infection from pneumonia using chest X-rays since they both have similar symptoms. Here the work depicts by comparing various CNN models and detects the differences in chest X-rays for the identification of diseases, with high accuracies. A new approach to the multi-classification method is accomplished. Preprocessing techniques such as histogram equalization and bilateral filtering are used to enhance the quality of chest X-ray images [1]. The proposed system is experienced with the CNN architectures such as VGG16 and InceptionV3 which are used for multiclassification. It is noted that InceptionV3 is less expensive. The comparison is done between both the models, and the accuracies are compared to identify the best model. VGG16 attained an accuracy of 88%, and InceptionV3 attained the highest accuracy with 93%. All architecture performances are compared using various classification metrics forestimating the performance of DL techniques.