Developing Deep Learning Models for Classifying Medical Imaging Data

The rapid evolution of deep learning has fundamentally transformed medical image analysis, enabling unprecedented advancements in diagnostic accuracy, disease detection, and clinical decision support. This study presents a comprehensive investigation into the development, optimization, and evaluation of deep learning models specifically tailored for the classification of medical imaging data across various modalities, including radiographs (X-rays), computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound. The research addresses critical challenges associated with data heterogeneity, limited labeled samples, class imbalance, and the need for explainability in clinical contexts. A hybrid methodology was adopted, combining convolutional neural networks (CNNs), transfer learning techniques, and attention mechanisms to develop robust classifiers capable of distinguishing between pathological and non-pathological images with high precision. Publicly available datasets such as ChestX-ray14, BraTS, and NIH's DeepLesion were used to ensure diversity and generalizability. The models were evaluated using rigorous metrics including accuracy, area under the receiver operating characteristic curve (AUC-ROC), sensitivity, specificity, and F1-score. The best-performing architecture—based on an ensemble of ResNet50 and EfficientNet-B4—achieved an average classification accuracy of 94.3% and an AUC-ROC of 0.97 across multiple tasks, outperforming traditional machine learning baselines. Furthermore, the study explores the integration of Grad-CAM and SHAP interpretability frameworks to visualize and validate the model’s decision-making process, enhancing clinical trust and adoption potential. A critical component of the research also includes a comparative analysis of training paradigms under supervised, semi-supervised, and self-supervised learning conditions, demonstrating that hybrid semi-supervised approaches significantly reduce dependency on large annotated datasets without compromising model performance. This work contributes to the growing body of knowledge in AI-driven healthcare by offering a scalable and generalizable framework for automated image classification, addressing both technical performance and ethical transparency. The findings have far-reaching implications for radiology, oncology, and pathology, potentially enabling faster diagnosis, reduced diagnostic error, and improved healthcare accessibility, particularly in low-resource settings. Future research directions include integrating multimodal imaging data, leveraging federated learning for privacy-preserving training, and extending the framework to real-time clinical deployment.