Classification Identification of Coronary Heart Disease using Transfer Learning

In this paper, we embark on a detailed exploration of six seminal deep learning models that are widely recognized for their effectiveness in classification tasks. We elucidate not only their foundational principles but also delve into their intricate network structures. Our approach incorporates advanced transfer learning methodologies, featuring the strategic freezing of weights in base models and the innovative amalgamation of these models with custom architectures to forge complete neural networks. These networks are rigorously trained on a carefully curated small-sample medical imaging dataset, designed to challenge and refine their capabilities. The climax of our investigation involves a meticulous examination of how these models fare on the unique medical data set compiled exclusively for this research. We meticulously track and analyze their accuracy and loss metrics as they evolve across numerous training epochs. This thorough analysis provides invaluable insights into the models' robustness and adaptability, shedding light on their generalization performance in real-world medical applications. Through this comprehensive study, we aim to contribute valuable knowledge to the field of medical image analysis, highlighting the potential and limitations of deep learning models in this domain.