A Unified Deep Learning Framework for Multi-Chronic Disease Classification and Stage Prediction Using Medical Imaging

This paper integrates Internet of Medical Things (IoMT) and deep learning to predict chronic diseases from medical images for early diagnosis in Healthcare 5.0. Traditional methods like Support Vector Machine (SVM) require manual feature extraction and have lower accuracy for complex image data. The proposed system uses DenseNet-201 deep learning model for automatic feature extraction and disease classification. Medical datasets such as pancreatic ultrasound, brain MRI, and Alzheimer’s MRI images are used for analysis. The system provides fast, accurate, and automated diagnosis with reduced manual errors, implemented using MATLAB 2020a or above.