DEEP LEARNING MODELS FOR EARLY DISEASE DETECTION AND DIAGNOSIS

Early and accurate disease detection is critical for timely clinical intervention, improved patient outcomes, and optimized healthcare resource utilization. Recent advances in deep learning have revolutionized the field of medical diagnostics by enabling automated analysis of complex, multi-modal biomedical data such as medical images, genomics, and electronic health records. This paper presents a comprehensive review and empirical evaluation of state-of-the-art deep learning architectures including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformers, and hybrid models for early identification and staging of conditions such as cancer, Alzheimer's disease, and chronic kidney disease. Using large, diverse datasets, we highlight how specialized networks and hybrid frameworks can enhance feature extraction, improve classification accuracy, and identify subtle early-stage biomarkers, with some models achieving accuracies exceeding 95%. Practical implications, including improved precision and recall in real-world clinical scenarios, are discussed alongside challenges such as data heterogeneity, generalizability, and model interpretability. The study underscores the transformative potential of deep learning in early disease detection and advocates for continued efforts in developing robust, explainable AI systems to support clinical decision-making and personalized medicine.