Brain Tumor Classification using MobileNet Convolutional Block Attention Module

Muhammad Suleman  Memon; Mumtaz  Qabulio; Samia Aijaz  Siddiqui; Mansoor Ali

doi:10.62019/f85cwx57

Authors

Muhammad Suleman Memon Department of Information Technology, Dadu Campus, University of Sindh, Dadu, Pakistan
Dr.Mumtaz Qabulio Department of Software Engineering, Faculty of Engineering & Technology, University of Sindh, Jamshoro, Pakistan.
Samia Aijaz Siddiqui Department of Computer System Engineering, Dawood University of Engineering and Technology, Karachi, Pakistan.
Mansoor Ali Department of Computer System Engineering, Dawood University of Engineering and Technology, Karachi, Pakistan.

DOI:

https://doi.org/10.62019/f85cwx57

Abstract

Brain tumors are very deadly disease. The first step to investigating brain tumors is CT scan or MRI. The MRI images are then referenced for further investigation. It is very important to locate the tumors effectively. The manual methods of observing tumor location and shape can lead to wrong treatment. Automated methods such as Deep Learning helps to evaluate the type of the tumor and shape of the tumor effectively. In this research we used MobileNet as the pretrained network for the feature extraction process and convolutional block attention model for the visualization. Current study trained four models MobileNet CBAM, MobileNet SE, MobileNet ECA, MobileNet. The model was trained on Figshare dataset which include four classes, glioma, meningioma, no tumor and pituitary. We used 4,557 images to train the model and 1,311 images to test it. The proposed model achieved an accuracy of 95%. The CBAM attention-based visual explainer was used to show where the model was focusing its predictions, which made the model easier to understand. This method is a useful tool in clinical settings where quick and accurate diagnosis is important.

Author Biographies

Muhammad Suleman Memon, Department of Information Technology, Dadu Campus, University of Sindh, Dadu, Pakistan

Dr. Muhammad Suleman Memon is currently working as an Assistant Professor and Incharge of the Department of Information Technology at the University of Sindh, Dadu Campus, Pakistan. He received his Ph.D. in Computer Systems Engineering with a specialization in Deep Learning. Dr. Memon has more than 12 years of teaching and research experience in the field of Computer Science. His research interests include Deep Learning, Computer Vision, Medical Image Analysis, and Artificial Intelligence applications in agriculture and healthcare. He has authored over 22 research publications in reputed national and international journals. Dr. Memon also serves as an editorial board member of two international journals. His academic contributions reflect his dedication to advancing AI-driven solutions for real-world problems.
Dr.Mumtaz Qabulio, Department of Software Engineering, Faculty of Engineering & Technology, University of Sindh, Jamshoro, Pakistan.

Dr. Mumtaz Qabulio is currently serving as an Assistant Professor in the Department of Software Engineering, Faculty of Engineering & Technology, University of Sindh, Jamshoro, Pakistan. She obtained her Ph.D. in Computer Science from the University of Sindh, Jamshoro, in 2019. With over 13 years of teaching and research experience, Dr. Qabulio has made significant contributions in the areas of Wireless Sensor Networks (WSNs), Machine Learning, Deep Learning, and the Internet of Things (IoT). She has authored more than 20 research papers in reputed national and international journals and conferences. Her scholarly work has earned her an h-index of 5 on Google Scholar, reflecting her influence in the research community.
Samia Aijaz Siddiqui, Department of Computer System Engineering, Dawood University of Engineering and Technology, Karachi, Pakistan.

Assistant Professor

Department of Computer System Engineering, Dawood University of Engineering and Technology
Mansoor Ali, Department of Computer System Engineering, Dawood University of Engineering and Technology, Karachi, Pakistan.

Lecturer, Department of Computer System Engineering,Dawood University of Engineering and Technology Karachi

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