Deep Convolutional Neural Networks Model-based Brain Tumor Detection in Brain MRI Images

TL;DR

This paper presents a deep convolutional neural network that accurately detects brain tumors in MRI images, significantly aiding clinical diagnosis with high precision and speed.

Contribution

The study develops a novel DCNN model that outperforms traditional methods in brain tumor detection from MRI images, achieving over 96% accuracy.

Findings

Model achieves 96% accuracy in tumor detection.

Outperforms conventional diagnostic methods.

High precision, sensitivity, and AUC scores.

Abstract

Diagnosing Brain Tumor with the aid of Magnetic Resonance Imaging (MRI) has gained enormous prominence over the years, primarily in the field of medical science. Detection and/or partitioning of brain tumors solely with the aid of MR imaging is achieved at the cost of immense time and effort and demands a lot of expertise from engaged personnel. This substantiates the necessity of fabricating an autonomous model brain tumor diagnosis. Our work involves implementing a deep convolutional neural network (DCNN) for diagnosing brain tumors from MR images. The dataset used in this paper consists of 253 brain MR images where 155 images are reported to have tumors. Our model can single out the MR images with tumors with an overall accuracy of 96%. The model outperformed the existing conventional methods for the diagnosis of brain tumor in the test dataset (Precision = 0.93, Sensitivity = 1.00, and F1-score = 0.97). Moreover, the proposed model's average precision-recall score is 0.93, Cohen's Kappa 0.91, and AUC 0.95. Therefore, the proposed model can help clinical experts verify whether the patient has a brain tumor and, consequently, accelerate the treatment procedure.