Deep Learning for Early Disease Detection in Medical Imaging

Abstract

Early diagnosis of diseases plays a crucial role in increasing survival chances, lowering healthcare expenses, and ensuring prompt treatment. Advances in artificial intelligence, particularly deep learning, have revolutionized medical imaging by providing robust methods for automated detection, classification, and prediction of various conditions. This study explores the application of advanced deep learning models—including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transfer Learning—in interpreting medical scans such as X-rays, CT, MRI, and ultrasound. The proposed approach leverages CNN-based architectures for accurate feature extraction and image classification, supporting the early detection of conditions such as cancer, pneumonia, tuberculosis, and diabetic retinopathy. Furthermore, this work critically reviews existing studies, identifies ongoing challenges related to data privacy, model interpretability, and generalization across diverse datasets, and proposes a framework designed to deliver reliable, scalable, and explainable solutions for early disease detection in clinical practice.