Detection and Classification of Pneumonia, Covid-19 and Tuberculosis Using Deep Learning Methods

The deep learning literature is presented in this publication. in the classification of respiratory diseases, focusing on pneumonia, COVID-19, and tuberculosis. The review explores the potential of various Complex patterns that can be extracted from medical images using advanced neural network designs, like recurrent neural network designs (RNNs) and convolutional neural networks (CNNs). It evaluates the efficacy of these models using a range of datasets and considers limitations like interpretability, class disparity, and data scarcity. The study also discusses integrating multimodal data sources to enhance diagnostic accuracy. A comparative analysis of deep learning models highlights ResNet-18's superior performance in classifying CT scans of tuberculosis, pneumonia, and COVID-COVID-19. The findings suggest that deep learning models can aid radiologists in accurately diagnosing respiratory diseases from CT images, potentially transforming early detection and classification methods.