Transforming Diagnosis: Machine Learning for Proactive Disease Detection in Medical Imaging

Abstract

Early and accurate disease detection is crucial for improving patient outcomes and optimizing treatment strategies. This project proposes a machine learning-based system leveraging Convolutional Neural Networks (CNNs) with the VGG16 architecture to detect and classify three major medical conditions—brain disorders (such as tumors), skin diseases (including melanoma and other dermatoses), and liver abnormalities—from medical images. By utilizing the pre-trained VGG16 model with fine-tuning on curated datasets, the system achieves high accuracy in identifying disease-specific patterns in MRI, dermatoscopic, and CT images. The model is trained separately on datasets corresponding to each disease category and then integrated into a unified framework that predicts the type and presence of disease based on input images. The approach enhances diagnostic precision, reduces dependency on manual analysis, and accelerates clinical decision-making. This project demonstrates the potential of deep learning to revolutionize early detection systems in healthcare and lays the foundation for scalable, AI-assisted diagnostic tools.