Brain Tumor Classification: Leveraging Transfer Learning via EfficientNet-B0 Pretrained Model | ||||
| International Integrated Intelligent Systems | ||||
| Article 4, Volume 2, Issue 1, January 2025 PDF (516.07 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/iiis.2025.292441.1034 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Reem Tamer Hegazy  1; Salmeen Khalil Khalifa2; Rodina Amr Mortada3; Basel A. Amin4; Amr Abd Elfattah4
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| 1Department of Artificial Intelligence, Misr university for science and technology | ||||
| 2Departament of computer science,Misr university for science and technology | ||||
| 3Department of artificial intelligence,Misr university for science and technology | ||||
| 4Department of computer science, Misr university for science and technology | ||||
| Abstract | ||||
| Abstract— Brain tumor classification from MRI scans is an essential task in medical diagnostics, enhancing the precision and speed of treatment planning. This project introduces a deep learning model that automates the classification of brain tumors by leveraging a pre-trained convolutional neural network (CNN). The model processes MRI images and categorizes them into one of four possible classes: glioma, meningioma, pituitary tumor, or no tumor. By utilizing the EffnetB0 pretrained model, our approach benefits from learned features on a broad range of visual data, allowing for robust feature extraction even with a limited number of medical images. The dataset consists of MRI scans, each labeled according to the tumor type(glimona, meningioma, no tumor,pitutary ), facilitating supervised learning. The effectiveness of the model is assessed based on accuracy, precision, and recall metrics, aiming to support radiologists by providing a reliable preliminary diagnostic tool that improves the diagnostic workflow for brain tumors. | ||||
| Keywords | ||||
| Brain tumor classification; pre-trained models; Deep learning | ||||
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