A Generalized Deep Learning Model for Simultaneous Detection of Bone, Brain, Lung, and Skin Diseases from Medical Images

Early and accurate diagnosis of multiple diseases using medical imaging is critical for effective healthcare delivery. This paper proposes a generalized deep learning framework for simultaneous detection of bone fractures, brain tumors, lung cancer, and skin lesions using heterogeneous medical image datasets. A lightweight Convolutional Neural Network (CNN) is designed to extract hierarchical features and perform multi-class classification. The model achieves near-perfect accuracy (100%), with excellent precision, recall, and F1-scores across all classes. ROC-AUC scores exceeding 0.99 further validate the model’s discriminative capability. Despite strong performance, cross-validation results highlight the need for improved generalization. The proposed system demonstrates strong potential for clinical decision support applications.