Open Access

ARTICLE

Analysis of Brain MRI: AI-Assisted Healthcare Framework for the Smart Cities

Walid El-Shafai^1,*, Randa Ali¹, Ahmed Sedik², Taha El-Sayed Taha¹, Mohammed Abd-Elnaby³, Fathi E. Abd El-Samie¹

1 Department Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menoufia, 32952, Egypt
2 Department of ROBOTICS and Intelligent Machines, Faculty of Artificial Intelligence, Kafrelsheikh University, Kafrelsheikh, Egypt
3 Department of Computer Engineering, College of Computers and Information Technology, Taif University, Taif, 21944, Saudi Arabia

* Corresponding Author: Walid El-Shafai. Email:

Intelligent Automation & Soft Computing 2023, 35(2), 1843-1856. https://doi.org/10.32604/iasc.2023.019198

Received 06 April 2021; Accepted 30 August 2021; Issue published 19 July 2022

Abstract

The use of intelligent machines to work and react like humans is vital in emerging smart cities. Computer-aided analysis of complex and huge MRI (Magnetic Resonance Imaging) scans is very important in healthcare applications. Among AI (Artificial Intelligence) driven healthcare applications, tumor detection is one of the contemporary research fields that have become attractive to researchers. There are several modalities of imaging performed on the brain for the purpose of tumor detection. This paper offers a deep learning approach for detecting brain tumors from MR (Magnetic Resonance) images based on changes in the division of the training and testing data and the structure of the CNN (Convolutional Neural Network) layers. The proposed approach is carried out on a brain tumor dataset from the National Centre of Image-Guided Therapy, including about 4700 MRI images of ten brain tumor cases with both normal and abnormal states. The dataset is divided into test, and train subsets with a ratio of the training set to the validation set of 70:30. The main contribution of this paper is introducing an optimum deep learning structure of CNN layers. The simulation results are obtained for 50 epochs in the training phase. The simulation results reveal that the optimum CNN architecture consists of four layers.

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Keywords

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