An automated metaheuristic-optimized approach for diagnosing and classifying brain tumors based on a convolutional neural network

Brain tumors must be classified to determine their severity and appropriate therapy. Applying Artificial Intelligence to medical imaging has enabled remarkable developments. The presented framework classifies patients with brain tumors with high accuracy and efficiency using CNN, pre-trained models, and the Manta Ray Foraging Optimization (MRFO) algorithm on X-ray and MRI images. Additionally, the CNN and Transfer Learning (TL) hyperparameters will be optimized through MRFO, resulting in improved performance of the pretrained model. Two public datasets from Kaggle were used to build the models. The first dataset consists of two X-ray classes, while the 2 nd dataset includes three contrast-enhanced T1-weighted MRI classes. First, a patient should be diagnosed as "Healthy" (or "Tumor"). When the scan returns the result "Healthy," the patient has no abnormalities in their brain. If a scan reveals that the patient has a tumor, an MRI will be performed on them. After that, the type of tumor (i.e., meningioma, pituitary, and glioma) will be identified using the second proposed classifier. A comparative analysis of the models used in the two-class dataset showed that VGG16's pre-trained model outperformed other models. Besides, the Xception pre-trained model achieved the best results in the three-class dataset. A manual review of misclassifications was conducted to determine the reasons for the misclassifications and correct them. The evaluation of the suggested architecture yielded an accuracy of 99.96% for X-rays and 98.64% for T1-weighted contrast-enhanced MRIs. The proposed deep learning framework outperformed most current deep learning models.