Paper Title
PREDICTING LUNG DISEASES FROM X-RAYS: A DENSE CONVOLUTIONAL NEURAL NETWORK APPROACH

Abstract
Early and accurate diagnosis of lung diseases from chest X-rays plays a crucial role in improving patient outcomes and reducing healthcare costs. In this study, we propose a Dense Convolutional Neural Network (CNN) model for the automated prediction of lung diseases based on X-ray images. The proposed model integrates dense connections within convolutional layers, allowing for efficient feature reuse and gradient flow throughout the network. We utilize a comprehensive dataset comprising X-ray images representing various lung diseases, including pneumonia, tuberculosis, and lung cancer, as well as normal cases. The dataset is preprocessed to enhance the quality of input images and normalize intensity levels. Through extensive experimentation and validation, we demonstrate the efficacy of our Dense CNN model in accurately classifying lung diseases. We evaluate the model's performance using metrics such as accuracy, precision, recall, and F1-score, achieving promising results compared to existing approaches. Keywords - CNN, Lungs Disease, X-Ray