Multi-Classification of Brain Tumor MRI Images Using Deep Convolutional Neural Network with Fully Optimized Framework

• Published in: Iranian journal of science and technology. Transactions of electrical engineering Vol. 45; no. 3; pp. 1015 - 1036
• Main Author: Irmak, Emrah
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 2021; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Artificial neural networks; Biopsy; Brain; Brain cancer; Brain tumors; Classification; Diagnosis; Electrical Engineering; Engineering; Glioma; Image classification; Machine learning; Magnetic resonance imaging; Mathematical models; Medical imaging; Meningioma; Metastases; Model accuracy; Neural networks; Optimization; Parameters; Physicians; Pituitary; Research Paper; Tumors; Deep learning; Grid search; Hyper-parameter optimization; Brain tumor image classification; Tumor grading; Convolutional neural network
• ISSN: 2228-6179; 2364-1827
• DOI: 10.1007/s40998-021-00426-9

Brain tumor diagnosis and classification still rely on histopathological analysis of biopsy specimens today. The current method is invasive, time-consuming and prone to manual errors. These disadvantages show how essential it is to perform a fully automated method for multi-classification of brain tumors based on deep learning. This paper aims to make multi-classification of brain tumors for the early diagnosis purposes using convolutional neural network (CNN). Three different CNN models are proposed for three different classification tasks. Brain tumor detection is achieved with 99.33% accuracy using the first CNN model. The second CNN model can classify the brain tumor into five brain tumor types as normal, glioma, meningioma, pituitary and metastatic with an accuracy of 92.66%. The third CNN model can classify the brain tumors into three grades as Grade II, Grade III and Grade IV with an accuracy of 98.14%. All the important hyper-parameters of CNN models are automatically designated using the grid search optimization algorithm. To the best of author’s knowledge, this is the first study for multi-classification of brain tumor MRI images using CNN whose almost all hyper-parameters are tuned by the grid search optimizer. The proposed CNN models are compared with other popular state-of-the-art CNN models such as AlexNet, Inceptionv3, ResNet-50, VGG-16 and GoogleNet. Satisfactory classification results are obtained using large and publicly available clinical datasets. The proposed CNN models can be employed to assist physicians and radiologists in validating their initial screening for brain tumor multi-classification purposes.