Development and Assessing a Competitive Deep Learning Framework forBrain Tumor classification Based on MRI Images
سال انتشار: 1402
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 108
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شناسه ملی سند علمی:
DMECONF09_096
تاریخ نمایه سازی: 12 اردیبهشت 1403
چکیده مقاله:
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
کلیدواژه ها:
نویسندگان
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