Published Paper Details:

The performance of convolutional neural networks (CNN) in the diagnosis of pneumonia and malaria, two serious illnesses, is examined in this article. The research compares the efficacy of various CNN architectures in identifying these diseases using a dataset of medical images that is openly accessible. With some architectures performing better than others, the findings show that CNNs can achieve high accuracy in the diagnosis of both malaria and pneumonia. According to the research, CNNs may prove to be an invaluable instrument for the rapid and accurate diagnosis of these diseases, which could lead to better patient outcomes and even lifesaving outcomes. Disease prediction system based on Convolutional Neural Network (CNN) (class of Deep Learning (DL) algorithm) with feature extraction and classification. Malaria spreads from the bites of mosquitoes and are transmitted to the people by the parasites. Pneumonia is an interstitial lung disease and affected to the childrens who were mostly less than two years old.