Optimized CNN Using Manta-Ray Foraging Optimization for Brain Tumour Detection.

Brain tumors pose a significant medical challenge, demanding early detection and diagnosis to enhance patient outcomes. However, existing brain tumor detection methods often suffer from inefficiency and inaccuracy. Presently, deep learning models have gained prominence in classifying brain MRI images due to their superior accuracy compared to traditional classifiers. Nonetheless, optimizing hyperparameters manually to achieve precise image classification is a daunting and time-consuming task. To address these challenges, we present a novel approach in this paper—the Manta Ray Foraging Optimized Convolutional Neural Network (MRFO-CNN) model to classify brain MRIs into tumorous and non-tumorous categories. We leverage the Manta Ray Foraging Optimization technique to automatically determine the optimal hyperparameters, specifically batch size and epoch settings. Our model is trained on a dataset comprising 3000 brain MR images and validated on an additional 351 brain MR images. The results unequivocally demonstrate the superiority of our proposed MRFO-CNN model over conventional CNN approaches, achieving an impressive training accuracy of 99.3% and a validation accuracy of 98.7%. This research not only showcases the potential of deep learning in brain tumours classification but also underscores the efficiency and effectiveness of automated hyperparameter optimization in medical image analysis. [ABSTRACT FROM AUTHOR]