Multiple Infectious Disease Diagnosis and Detection Using Advanced CNN Models

In the modern medical sector, deep learning methods are proving exceptionally effective in enhancing disease detection and classification accuracy through the analysis of medical imagery. The paper introduces an innovative framework for identifying infectious diseases using advanced convolutional neural network (CNN) models. The dataset encompasses diverse images of diseases like pneumonia, COVID-19, lung opacity, and MERS which have been sourced from various repositories. These images are transformed into grayscale and subjected to data augmentation techniques like horizontal and vertical flipping. The pre-processed images are then employed to train the models like VGG16, ResNet152V2, DenseNet169, and MobileNetV2. Through comprehensive evaluation using metrics such as loss, accuracy, recall, precision, and F1 score, the paper reveals that MobileNetV2 stands out by attaining remarkable accuracy and recall rates of 88.09% and 88.56%, respectively, in detecting and classifying infectious diseases. The model's potential in assisting medical practitioners with diagnoses and interventions is thereby underscored.