Classification of Brain Tumor Using Hybrid Pre-Trained Networks and MRMR Algorithm

Authors

Mustafa Ridha Al-Yasari, Optical Techniques Department, College of Health and Medical Techniques, Al-Mustaqbal University, 51001, Babylon, IraqFollow
Mohammed Qasim Alazzawi, Optical Techniques Department, College of Health and Medical Techniques, Al-Mustaqbal University, 51001, Babylon, IraqFollow
Sarmad Jawad, Optical Techniques Department, College of Health and Medical Techniques, Al-Mustaqbal University, 51001, Babylon, IraqFollow
Mohammed Alshujairi, Optical Techniques Department, College of Health and Medical Techniques, Al-Mustaqbal University, 51001, Babylon, IraqFollow
Zamzam Ali Abood Noory Al-Shafiee, Anesthesia Techniques Department, College of Health and Medical Techniques, Al-Mustaqbal University, 51001, Babylon, Iraq
Jaber Parchami, Department of Electrical Engineering, Sadjad University of Technology, Mashhad, Islamic Republic of Iran, Iran
Heba. G. Abdelzaher, Department of Clinical Pharmacy, Faculty of Pharmacy, Minia University, 61519 Minia, Egypt
M. A. Abdelzaher, Environmental Science and Industrial Development Department, Faculty of Postgraduate Studies for Advanced Sciences, Beni-Suef University, Beni-Suef 62511, EgyptFollow

Abstract

Brain tumor classification is one of the crucial uses of medical image processing. A longer life is possible if the tumor is correctly and promptly diagnosed. Because manually segmenting brain tumors for cancer diagnosis is difficult and time-consuming, automatic classification of brain tumor images is necessary for this task. Pre-processing, feature extraction, feature selection, and classification are the four stages of the general framework for automatic tumor detection from MRI images. The method for automatic brain tumor classification described in this research combines machine learning and deep learning algorithms. We used three pre-trained deep networks to extract the most detailed information from MRI images. ResNet, AlexNet, and GoogleNet are the networks that were utilized for feature extraction for the diagnosis of brain tumors, the classification model has been a support vector machine (SVM). Additionally, in this research, the best feature vector was chosen using the MRMR algorithm to improve classification speed and accuracy. The BRATS database is used to provide the training dataset. The BRATS validation dataset showed promising results for the investigated method. This method's complete tumor classification accuracy on experimental data is 99.5% on average in this dataset.

Recommended Citation

Al-Yasari, Mustafa Ridha; Alazzawi, Mohammed Qasim; Jawad, Sarmad; Alshujairi, Mohammed; Al-Shafiee, Zamzam Ali Abood Noory; Parchami, Jaber; Abdelzaher, Heba. G.; and Abdelzaher, M. A. (2025) "Classification of Brain Tumor Using Hybrid Pre-Trained Networks and MRMR Algorithm," Al-Mustaqbal Journal of Pharmaceutical and Medical Sciences: Vol. 3 : Iss. 1 , Article 4.
Available at: https://doi.org/10.62846/3006-5909.1024