Classification of Brain Tumors Using MRI Images Based on Fine-Tuned Pretrained Models

Aliyu Tetengi Ibrahim, Mohammed Tukur Mohammed, Mohammed Awal Suleiman, Abdulaziz Bello Kofa and Idris Muhammad Ladan
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Aliyu Tetengi Ibrahim: Department of Computer Science, Ahmadu Bello University, Zaria, Nigeria
Mohammed Tukur Mohammed: National Cereals Research Institute Badeggi, Nigeria.
Mohammed Awal Suleiman: Computer Science Department, Federal Polytechnic Bida, Nigeria
Abdulaziz Bello Kofa: Technical Services, Galaxy Backbone Limited, Nigeria
Idris Muhammad Ladan: Department of Computer Science, UNITe S Cisco Networking Academy, Nigeria

International Journal of Research and Scientific Innovation, 2024, vol. 11, issue 3, 353-368

Abstract: Brain tumors are frequently diagnosed malignant growths found across all age groups, and they pose a significant threat if not detected promptly. Assessing their severity presents a challenge for radiologists during health monitoring and automated identification. Detecting and categorizing affected areas using Magnetic Resonance Imaging (MRI) scans is crucial. Various types of tumors such as glioma, meningioma, pituitary, and benign tumors exist. Additionally, manual procedures are inefficient, error-prone, and time-consuming. Thus, there’s a pressing need for a reliable solution to ensure precise diagnosis. A CNN (Convolutional Neural Network), a cutting-edge technique in deep learning, has been employed for brain tumor detection using MRI images. However, challenges persist in training due to a limited number of images, which is insufficient for CNN’s requirements. To address this limitation, transfer learning techniques have been utilized. Additionally, image augmentation methods have been applied to increase the dataset size and improve model performance. This study introduces a modified deep CNN incorporating transfer learning techniques and a learning rate scheduler for brain tumor classification. Four pretrained models—Xception, Dense Net 201, Mobile Net, and Inception Res Net V2—serve as base models. Each model is trained individually multiple times, both with and without a learning rate scheduler, while employing two optimizers—Adam and Adamax—independently to assess performance. The training process is conducted in four stages: (i) using a static learning rate with the Adam optimizer, (ii) using a static learning rate with the Adamax optimizer, (iii) employing a dynamic learning rate with the Adam optimizer, and (iv) utilizing a dynamic learning rate with the Adamax optimizer. To enhance the model’s depth and complexity for extracting more relevant features from the images, two additional dense layers are incorporated into each of the models, all utilizing Leaky Re LU as the activation function. The proposed model is trained and validated using an MRI image dataset, which is publicly available. Following test result analysis, it was found that Inception Res Net V2, without the application of a learning rate scheduler, outperformed other models, achieving an accuracy of 96.58%. Additionally, it attained precision, recall, and F1 score values of 96.59%, 96.58%, and 96.57% respectively.

Date: 2024
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