Innovative Lung Cancer Diagnosis Using the AlReM-Net Hybrid Approach

As one of the primary causes of cancer-related death globally, lung cancer requires precise and effective diagnostic instruments for early identification and categorization. This paper presents AIReM-Net, a hybrid framework that combines Multi-Class Support Vector Machines (SVM) for classification, Reservoir Computing (RC) for dynamic pattern analysis, and AlexNet for feature extraction. The suggested methodology’s clinical relevance and computational efficiency are highlighted by its $98.7 \%$ sensitivity, $100 \%$ specificity, and $98.8 \%$ accuracy. AIReMNet’s detection module successfully overcomes the drawbacks of current diagnostic systems by preprocessing CT scans, segmenting lung nodules, and extracting essential information for precise cancer identification. Using both spatial and dynamic information for dependable classification, the combination of AlexNet, RC, and SVM guarantees accurate and robust results. With continued attempts to improve the system by incorporating a thorough classification module for real-time clinical applications, these developments offer a computationally effective and clinically appropriate approach for lung cancer diagnosis.