A novel hybrid model for brain tumor analysis with CNN and Moth Flame Optimization

Abstract

Early and accurate detection of brain tumors is vital for improving patient outcomes and treatment decisions. This study presents a Hybrid Brain Tumor Analysis (BTA) framework that integrates Moth Flame Optimization (MFO) and Convolutional Neural Networks (CNNs) for tumor identification, segmentation, and classification using MRI scans. A hybrid segmentation approach is employed, combining K-means clustering with MFO and a custom fitness function to extract tumor regions. Feature extraction is followed by MFO-based feature selection to reduce dimensionality and enhance classification performance. The refined features are used to train a custom CNN architecture, BTA-Net, for classifying tumors into meningioma, glioma, and pituitary types. The proposed model achieves a 3.22 % improvement in classification accuracy compared to baseline methods, along with notable gains in precision (4.07 %), recall (2.46 %), and F-measure (3.25 %). Statistical validation confirms the significance of these results, making the BTA framework a robust tool for automated brain tumor analysis.