A Co-Evolutionary Genetic Algorithm Approach to Optimizing Deep Learning for Brain Tumor Classification

Abstract

Brain tumors are among the deadliest diseases, leading researchers to focus on improving the accuracy of tumor classification—a critical task for prompt diagnosis and effective treatment. Recent advancements in brain tumor diagnosis have significantly increased the use of deep learning techniques, particularly pre-trained models, for classification tasks. These models serve as feature extractors or can be fine-tuned for specific tasks, reducing both training time and data requirements. However, achieving high accuracy in multi-class brain tumor classification remains a major challenge, driving continued research in this area. Key obstacles include the need for expert interpretation of deep learning model outputs and the difficulty of developing highly accurate categorization systems. Optimizing the hyperparameters of Convolutional Neural Network (CNN) architectures, especially those based on pre-trained models, plays a crucial role in improving training efficiency. Manual hyperparameter adjustment is time-consuming and often results in suboptimal outcomes. To address these challenges, we propose an advanced approach that combines transfer learning with enhanced coevolutionary algorithms. Specifically, we utilize EfficientNetB3 and DenseNet121 pre-trained models in conjunction with the Co-Evolutionary Genetic Algorithm (CEGA) to classify brain tumors into four categories: gliomas, meningiomas, pituitary adenomas, and no tumors. CEGA optimizes the hyperparameters, improving both convergence speed and accuracy. Experiments conducted on a Kaggle dataset demonstrate that CEGA-EfficientNetB3 achieved the highest accuracy of 99.39%, while CEGA-DenseNet121 attained 99.01%, both without data augmentation. These results outperform cutting-edge methods, offering a rapid and reliable method for brain tumor classification. This approach has great potential to support radiologists and physicians in making timely and accurate diagnoses.