Bridging Image Diagnostics with Advanced Computational Techniques for Hidden Feature Detection

This research presents a comprehensive methodology for efficient medical image segmentation in radiology by synergistically combining the strengths of MobileNet and U-Net architectures. The process begins with the collection and preprocessing of diverse medical image datasets, including CT scans, MRI images, and X-rays. Preprocessing involves resizing, normalization, and data augmentation to enhance model robustness. MobileNet, known for its computational efficiency, serves as the feature extraction backbone. It efficiently captures hierarchical features from input medical images while minimizing computational requirements. Simultaneously, a U-Net-style architecture is integrated to leverage its exceptional segmentation accuracy. Skip connections are introduced to connect corresponding layers in both MobileNet and U-Net, facilitating the transfer of low-level and high-level features. Feature maps from both components are fused at multiple levels, combining MobileNet's efficiency with U-Net's accuracy. map, representing the final segmentation output. The hybrid model is trained using annotated medical image data with loss functions like Dice loss or cross-entropy. Fine-tuning and hyperparameter optimization are performed to enhance performance. The proposed methodology is applied to real-world radiology scenarios, such as tumor segmentation, organ delineation, and lesion detection. It significantly outperforms traditional methods, demonstrating superior accuracy and computational efficiency.