Towards Smarter Healthcare Enhancing Medical Imaging And Diagnostic Precision Through Advanced Deep Learning Models

Abstract

Medical imaging has emerged as a critical enabler of early disease detection and precision diagnostics, yet limitations in conventional techniques often constrain their sensitivity, specificity, and interpretability. This study presents a deep learning–driven framework integrating convolutional neural networks (CNNs), transformer-based models, and multimodal fusion pipelines for enhanced medical imaging analysis. The proposed system was validated on benchmark MRI and CT datasets for tumor classification, lesion segmentation, and anomaly detection. Quantitative results demonstrated significant performance improvements, achieving an overall classification accuracy of 97.2%, a Dice similarity coefficient of 96.5%, and a recall of 95.7%, surpassing state-of-the-art baselines such as U-Net, ResNet, and DenseNet. Moreover, our framework reduced inference latency by 21% compared to traditional CNN pipelines, ensuring feasibility for real-time clinical workflows. The integration of fairness-aware evaluation and interpretability modules further enhanced clinical trust and transparency. These findings highlight the potential of advanced deep learning architectures to transform diagnostic imaging, delivering higher accuracy, computational efficiency, and clinical applicability.