A deep learning approach for brain tumor detection using magnetic resonance imaging

Al-Akhir Nayan (Chulalongkorn University)
Ahamad Nokib Mozumder (European University of Bangladesh)
Md. Rakibul Haque (European University of Bangladesh)
Fahim Hossain Sifat (University of Liberal Arts Bangladesh)
Khan Raqib Mahmud (University of Liberal Arts Bangladesh)
Abul Kalam Al Azad (University of Liberal Arts Bangladesh)
Muhammad Golam Kibria (University of Liberal Arts Bangladesh)

International Journal of Electrical and Computer Engineering
Vol 13, No 1: February 2023

The growth of abnormal cells in the brain’s tissue causes brain tumors. Brain tumors are considered one of the most dangerous disorders in children and adults. It develops quickly, and the patient’s survival prospects are slim if not appropriately treated. Proper treatment planning and precise diagnoses are essential to improving a patient’s life expectancy. Brain tumors are mainly diagnosed using magnetic resonance imaging (MRI). As part of a convolution neural network (CNN)-based illustration, an architecture containing five convolution layers, five max-pooling layers, a Flatten layer, and two dense layers has been proposed for detecting brain tumors from MRI images. The proposed model includes an automatic feature extractor, modified hidden layer architecture, and activation function. Several test cases were performed, and the proposed model achieved 98.6% accuracy and 97.8% precision score with a low cross-entropy rate. Compared with other approaches such as adjacent feature propagation network (AFPNet), mask region-based CNN (mask RCNN), YOLOv5, and Fourier CNN (FCNN), the proposed model has performed better in detecting brain tumors.