Abstract: Medical diagnosis has seen significant advancements with the application of deep learning, offering improved solutions for healthcare challenges. This study focuses on the detection and classification of three critical diseases: heart disease, pneumonia, and diabetic retinopathy (DR). Each disease presents distinct diagnostic complexities, including variations in data representation and the need for accurate predictions. To address these challenges, the proposed system integrates CNN, ResNet, MobileNet, and DenseNet architectures, forming a robust and efficient diagnostic framework.The proposed framework incorporates CNN, Resnet, MobileNet and DenseNet architectures to build a robust system capable of addressing these challenges. Users can leverage the system through a user-friendly interface designed for healthcare professionals, providing rapid and accurate disease classification. The experimental results validate the effectiveness of the proposed deep learning framework, positioning it as a valuable tool for assisting in early diagnosis and medical decision-making. The combination of state-of-the-art architectures ensures both accuracy and computational efficiency, making the system suitable for real-time clinical applications.
Keywords: Deep Learning, MobileNet, DenseNet, ResNet, Machine Learning.
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DOI:
10.17148/IJARCCE.2025.14271
