Efficient Medical Image Processing for Tumor Detection Using Hybrid CNN Framework

Medical image processing is a critical field that supports early diagnosis and treatment planning for various diseases, including tumors. Tumors, if detected at an early stage, can significantly improve patient outcomes. However, the complexity of medical imaging data, including noise, artifacts, and variations in tumor shapes and sizes, presents significant challenges. This research proposes an innovative framework, CNN-Extreme Learning Machine Hybrid Algorithm (CNELM-Hybrid). The framework leverages the feature extraction capabilities of Convolutional Neural Networks (CNN) and the ultra-fast classification efficiency of Extreme Learning Machine (ELM). CNELM-Hybrid integrates these techniques into a unified architecture, CNELM-Edge, to deliver a robust and scalable solution for real-time medical image analysis. Simulation analysis was carried out using performance metrics such as classification accuracy, processing speed, sensitivity and specificity. The results demonstrate that CNELM-Hybrid achieves superior performance and significantly reduced computational time. Furthermore, the hybrid model exhibited enhanced capability in handling complex and imbalanced datasets, which are common challenges in medical image processing. These findings underscore the potential of CNELM-Hybrid in advancing tumor detection methodologies and its applicability in real-time clinical environments.