Barat Barati - Department of Radiology technology, Shoushtar School of Medical Sciences, Shoushtar, Iran
Fariba farhadi Birgani - Department of Basic Sciences, Shoushtar Faculty of Medical Sciences, Shoushtar, Iran
Tahereh Navidifar - Department of Basic Sciences, Shoushtar Faculty of Medical Sciences, Shoushtar, Iran
seyed ali Mousavi - Department of Health, Shoushtar Faculty of Medical Science, Shoushtar, Iran
Karim Khoshgard - Department of Medical Physics, School of Medicine, Kermanshah University of Medical Sciences,Kermanshah, Iran

The objective of this study is to introduce a model with the same number of layers as AlexNet andZFNet models with less training data in order to be competitive in the development of automatedmedical image analysis and brain tumor classification using a set of brain MRI images. A total of۳۰۰۰ brain magnetic resonance (MR) images, both with and without tumor presence, were sourcedfrom the Kaggle site. This dataset was divided into ۲۴۰۰ images for training and ۶۰۰ for testing.The model, implemented using the Python programming language with TensorFlow and Keraslibraries, competes against AlexNet, ZFNet, and VGG۱۶. Performance was evaluated on the basisof several experimental criteria, including accuracy, recall, F۱ score, precision, area under thereceiver operating characteristic curve (AUC), and training time. A comparative analysis amongthe models was conducted based on these metrics. The proposed model demonstrated competitiveperformance, ranking after AlexNet and ZFNet but outperforming VGG۱۶. This study contributedto the evolution of efficient deep learning models for medical image analysis, particularly for braintumor classification

brain, tumor, deep learning, algorithm, classification