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Abstract
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Early detection plays a crucial role in the successful treatment of many life-threatening diseases, yet it remains one of the most challenging aspects of modern medicine. Conditions such as cancer, Alzheimer’s disease, cardiovascular disorders, and diabetic retinopathy often begin with extremely subtle visual or structural changes that can easily be missed in routine clinical assessments. Despite remarkable advancements in medical imaging technologies, the interpretation of these images still relies heavily on the expertise and experience of radiologists. This manual evaluation process, while invaluable, is inherently limited by human factors such as fatigue, workload, and perceptual variability. As healthcare systems generate millions of images each year, the demand for rapid, accurate, and consistent interpretation continues to exceed the capacity of medical staff. This mismatch has created a clear need for intelligent systems capable of supporting clinicians by identifying early abnormalities that might otherwise go unnoticed. Deep learning, with its powerful ability to recognize complex visual patterns, offers a promising solution for enhancing early diagnosis and ultimately improving patient outcomes . Moreover, the importance of this research becomes even more evident when considering global disparities in healthcare infrastructure. In many regions, particularly low- and middle-income countries, the shortage of experienced radiologists leads to delayed diagnosis, misinterpretation of images, and limited access to high-quality screening programs. An accurate and reliable AI-based tool could not only alleviate the diagnostic burden in technologically advanced medical centers but also provide essential support in underserved areas where specialized expertise is scarce. By developing a deep learning system that is sensitive enough to detect early-stage disease and transparent enough to be trusted by clinicians, this research aims to contribute to a more equitable
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