Brain Tumor/Mass Classification Framework Using Magnetic-Resonance-Imaging-Based Isolated and Developed Transfer Deep-Learning Model

Muhannad Faleh Alanazi; Muhammad Umair Ali; Shaik Javeed Hussain; Amad Zafar; Mohammed Mohatram; Muhammad Irfan; Raed AlRuwaili; Mubarak Alruwaili; Naif H. Ali; Anas Mohammad Albarrak

doi:10.3390/s22010372

journal article Open Access Jan 04, 2022

Brain Tumor/Mass Classification Framework Using Magnetic-Resonance-Imaging-Based Isolated and Developed Transfer Deep-Learning Model

Muhannad Faleh Alanazi Muhammad Umair Ali

Raed AlRuwaili Mubarak Alruwaili Naif H. Ali Anas Mohammad Albarrak

Sensors Vol. 22 No. 1 pp. 372 · MDPI AG

View at Publisher Save 10.3390/s22010372

Abstract

With the advancement in technology, machine learning can be applied to diagnose the mass/tumor in the brain using magnetic resonance imaging (MRI). This work proposes a novel developed transfer deep-learning model for the early diagnosis of brain tumors into their subclasses, such as pituitary, meningioma, and glioma. First, various layers of isolated convolutional-neural-network (CNN) models are built from scratch to check their performances for brain MRI images. Then, the 22-layer, binary-classification (tumor or no tumor) isolated-CNN model is re-utilized to re-adjust the neurons’ weights for classifying brain MRI images into tumor subclasses using the transfer-learning concept. As a result, the developed transfer-learned model has a high accuracy of 95.75% for the MRI images of the same MRI machine. Furthermore, the developed transfer-learned model has also been tested using the brain MRI images of another machine to validate its adaptability, general capability, and reliability for real-time application in the future. The results showed that the proposed model has a high accuracy of 96.89% for an unseen brain MRI dataset. Thus, the proposed deep-learning framework can help doctors and radiologists diagnose brain tumors early.

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Citations

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References

Details

Published: Jan 04, 2022
Vol/Issue: 22(1)
Pages: 372
License: View

Authors

M

Muhannad Faleh Alanazi

Radiology, Department of Internal Medicine, College of Medicine, Jouf University, Sakaka 72388, Saudi Arabia

M

Muhammad Umair Ali

Department of Unmanned Vehicle Engineering, Sejong University, Seoul 05006, Korea

S

Shaik Javeed Hussain

Department of Electrical and Electronics, Global College of Engineering and Technology, Muscat 112, Oman

A

Amad Zafar

Department of Electrical Engineering, The Ibadat International University, Islamabad 54590, Pakistan

M

Mohammed Mohatram

Department of Electrical and Electronics, Global College of Engineering and Technology, Muscat 112, Oman

M

Muhammad Irfan

Electrical Engineering Department, College of Engineering, Najran University, Najran 61441, Saudi Arabia

R

Raed AlRuwaili

Radiology, Department of Internal Medicine, College of Medicine, Jouf University, Sakaka 72388, Saudi Arabia

M

Mubarak Alruwaili

Radiology, Department of Internal Medicine, College of Medicine, Jouf University, Sakaka 72388, Saudi Arabia

N

Naif H. Ali

Department of Internal Medicine, Medical College, Najran University, Najran 61441, Saudi Arabia

A

Anas Mohammad Albarrak

Department of Internal Medicine, College of Medicine, Prince Sattam Bin Abdulaziz University, Alkharj 16278, Saudi Arabia

Cite This Article

Muhannad Faleh Alanazi, Muhammad Umair Ali, Shaik Javeed Hussain, et al. (2022). Brain Tumor/Mass Classification Framework Using Magnetic-Resonance-Imaging-Based Isolated and Developed Transfer Deep-Learning Model. Sensors, 22(1), 372. https://doi.org/10.3390/s22010372

Brain Tumor/Mass Classification Framework Using Magnetic-Resonance-Imaging-Based Isolated and Developed Transfer Deep-Learning Model

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