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

TL;DR

This paper presents a CNN-based model for brain tumor detection from MRI images, achieving high accuracy and outperforming existing methods in the field.

Contribution

A novel CNN architecture with an automatic feature extractor and modified layers for improved brain tumor detection accuracy from MRI images.

Findings

Achieved 98.6% accuracy and 97.8% precision.

Outperformed AFPNet, mask RCNN, YOLOv5, and FCNN.

Demonstrated effective automatic feature extraction.

Abstract

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.