European Journal of Biomedical and Pharmaceutical Sciences

DEEP LEARNING MODELS FOR EARLY DISEASE DETECTION AND DIAGNOSIS

*P. Srinivas, G. Divya, V. Deepti Kiranmayi, Amitha Rose

ABSTRACT

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.

Keywords: Deep Learning, Convolutional Neural Networks (CNNs), Medical Imaging, Genomics, Electronic Health Records (EHRs), Explainable AI, Personalized Medicine.