Brain Tumor Detection in Magnetic Resonance Imaging by Deep Convolutional Neural Network

Subject Areas : Electronic Engineering

Mitra Afsarinejad ¹ (Department of Electrical Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran)
Nabiollah Shiri ² (Department of Electrical Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran)
Ramin Barati ³ (Department of Electrical Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran)

Received: 2023-12-23 Accepted : 2024-01-16 Published : 2024-04-02

Keywords: Brain tumor, Convolutional neural network (CNN), Medical imaging, Deep learning, Image classification.,

Abstract :

In this paper, brain tumor detection is addressed through the application of advanced deep-learning techniques. The approach involves the development and training of a comprehensive convolutional neural network (CNN) architecture. Leveraging an extensive dataset of brain magnetic resonance imaging (MRI), the proposed model expresses its proficiency in the classification of normal brain tissue and tumor-affected regions. The architecture encompasses multiple layers, including convolutional, batch normalization, and pooling layers, culminating in a robust classification layer. Through rigorous training and optimization, the introduced CNN achieves a high level of accuracy in brain tumor classification. The effectiveness of the proposed model is showcased through comprehensive experimentation, demonstrating its potential to significantly contribute to the medical field’s efforts in precise brain tumor diagnosis.

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