Bio-inspired Heuristic Optimization-Based Cascaded Hybrid Network for Brain Cancer Screening

Brain cancer is among the most fatal diseases globally, requiring early detection and timely intervention to reduce mortality rates. Traditional diagnosis relies on oncologists manually examining MRI images for signs of cancer, a process that, while effective, is time-consuming and labor-intensive. Recently, artificial intelligence (AI)-driven computer vision has emerged as a practical solution for image analysis and diagnosis. However, previous AI methods often demanded high computational resources and large datasets to achieve sufficient accuracy. This paper introduces a cascaded network combining deep learning and traditional machine learning techniques, optimized using a bio-inspired heuristic algorithm, for brain cancer detection from MRI images. The proposed approach achieves a high accuracy of 99.16% using limited data and low-computation devices. The cascaded network leverages five popular CNN models for feature extraction, Bacterial Foraging Optimization (BFO) for optimal feature selection, and seven machine-learning models for cancer detection. Among these, the MobileNetV2BFO-KNN combination proved to be the most effective. The proposed method ensures efficiency, accuracy, scalability, and robustness, offering a reliable solution for brain cancer screening.