Brain Tumor Classification Using Convolutional Neural Network and Bitterling Fish Optimization Algorithm.
Authors
Affiliations (4)
Affiliations (4)
- Graduate School of Natural and Applied Sciences, Department of Electrical and Electronics Engineering, Gazi University, Ankara 06500, Turkey.
- Department of Computer Science, Faculty of Education, University of Telafer, Nineveh 41016, Iraq.
- Department of Electrical and Electronics Engineering, Gazi University, Ankara 06500, Turkey.
- Department of Electrical and Electronics Engineering, Istanbul Topkapi University, Istanbul 34662, Turkey.
Abstract
<b>Background/Objectives:</b> Brain tumor diagnosis using magnetic resonance imaging (MRI) plays an important role in clinical decision-making and treatment planning. However, manual interpretation of MRI scans is time-consuming and may result in variations among radiologists. This study aims to develop an automated multiclass brain tumor classification framework by integrating deep learning-based feature extraction with the Bitterling Fish Optimization (BFO) algorithm for effective feature selection. <b>Methods:</b> MRI images were first subjected to preprocessing to prepare them for deep learning analysis. Several pretrained convolutional neural network (CNN) architectures, including VGG16, VGG19, InceptionV3, ResNet50, EfficientNet, MobileNet, and ShuffleNet, were employed to extract informative deep features from the MRI images. The extracted features were then optimized using the BFO algorithm, which selected the most relevant features while reducing feature redundancy. The selected features were subsequently used for multiclass brain tumor classification. Model performance was evaluated using sensitivity, specificity, precision, accuracy, F1-score, and area under the curve (AUC). <b>Results:</b> The experimental results demonstrated that BFO-based feature selection improved the classification performance of the evaluated CNN architectures compared with their corresponding models without feature selection. Among the investigated CNN-optimizer combinations, the BFO-ShuffleNet framework achieved the best overall performance, obtaining 98.98% sensitivity, 98.99% specificity, 98.99% precision, 99.00% accuracy, and a 98.99% F1-score. These results indicate that BFO effectively identified the most discriminative features and enhanced the classification capability of the ShuffleNet architecture. <b>Conclusions:</b> The proposed deep learning and BFO-based framework provide an accurate and efficient approach for automated multiclass brain tumor classification from MRI images. The findings demonstrate that combining pretrained CNN models with BFO-based feature selection can reduce feature redundancy and improve diagnostic performance. In particular, BFO-ShuffleNet demonstrated the highest classification performance and shows considerable potential as a computer-aided diagnostic tool to support radiologists in brain tumor assessment and clinical decision-making.