Brain Tumor Detection and Classification Using Fine-Tuned CNN with ResNet50 and U-Net Model: A Study on TCGA-LGG and TCIA Dataset for MRI Applications

Author:

Asiri Abdullah A.1,Shaf Ahmad2ORCID,Ali Tariq2,Aamir Muhammad2ORCID,Irfan Muhammad3ORCID,Alqahtani Saeed1,Mehdar Khlood M.4,Halawani Hanan Talal5,Alghamdi Ali H.6,Alshamrani Abdullah Fahad A.7,Alqhtani Samar M.8ORCID

Affiliation:

1. Radiological Sciences Department, College of Applied Medical Sciences, Najran University, Najran 61441, Saudi Arabia

2. Department of Computer Science, Sahiwal Campus, COMSATS University Islamabad, Sahiwal 57000, Pakistan

3. Electrical Engineering Department, College of Engineering, Najran University, Najran 61441, Saudi Arabia

4. Anatomy Department, Medicine College, Najran University, Najran 61441, Saudi Arabia

5. Computer Science Department, College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia

6. Department of Radiological Sciences, Faculty of Applied Medical Sciences, The University of Tabuk, Tabuk 47512, Saudi Arabia

7. Department of Diagnostic Radiology Technology, College of Applied Medical Sciences, Taibah University, Madinah 42353, Saudi Arabia

8. Department of Information Systems, College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia

Abstract

Nowadays, brain tumors have become a leading cause of mortality worldwide. The brain cells in the tumor grow abnormally and badly affect the surrounding brain cells. These cells could be either cancerous or non-cancerous types, and their symptoms can vary depending on their location, size, and type. Due to its complex and varying structure, detecting and classifying the brain tumor accurately at the initial stages to avoid maximum death loss is challenging. This research proposes an improved fine-tuned model based on CNN with ResNet50 and U-Net to solve this problem. This model works on the publicly available dataset known as TCGA-LGG and TCIA. The dataset consists of 120 patients. The proposed CNN and fine-tuned ResNet50 model are used to detect and classify the tumor or no-tumor images. Furthermore, the U-Net model is integrated for the segmentation of the tumor regions correctly. The model performance evaluation metrics are accuracy, intersection over union, dice similarity coefficient, and similarity index. The results from fine-tuned ResNet50 model are IoU: 0.91, DSC: 0.95, SI: 0.95. In contrast, U-Net with ResNet50 outperforms all other models and correctly classified and segmented the tumor region.

Funder

Najran University

Publisher

MDPI AG

Subject

Paleontology,Space and Planetary Science,General Biochemistry, Genetics and Molecular Biology,Ecology, Evolution, Behavior and Systematics

Reference45 articles.

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5. Current prevalence of major cancer risk factors and screening test use in the United States: Disparities by education and race/ethnicity;Siegel;Cancer Epidemiol. Prev. Biomark.,2019

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