MRI intracranial Neoplasm classification using hybrid LOA-based deep learning classifier

Abstract

The detection and segmentation of brain tumors in MRI images are essential for accurate diagnosis and effective treatment planning. Current methodologies often struggle to maintain accuracy while efficiently processing large datasets. This research integrates advanced techniques in image preprocessing, feature extraction, and classification to improve brain tumor detection and segmentation. This proposed research employs Adaptive Median-Gaussian Filtering to reduce noise while preserving edges and fine details. For segmentation, we combine Kernel Density Estimation (KDE) with Fuzzy C-Means (FCM) clustering (KDFCM) to utilize local density variations for more precise results. Feature extraction is conducted using Histogram of Oriented Gradients (HOG), which is optimized with Particle Swarm Optimization (PSO) for efficient and robust image analysis. The classification utilizes the ResNeSt architecture with split-attention mechanisms, and the Lion Optimization Algorithm (LOA) fine-tunes the model parameters. The simulations were conducted using MATLAB 2021a. Experimental results demonstrate the effectiveness of the proposed methods, achieving high accuracy, precision, recall, and F-measure across various datasets, including the X-ray image dataset, FigShare and BTC Magnetic Resonance Imaging (MRI) datasets. The proposed algorithm achieved 99.41 % accuracy, 99.57 % precision, 99.12 % recall, and 99.52 % F-measure for FigShare Data, and 99.72 % accuracy, 99.85 % precision, 99.25 % recall, and 99.67 % F-measure for BTC Data, outperforming existing DL technology. This research highlights the potential of these methods for reliable and efficient brain tumor detection.