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Journals / Bitlis Eren Üniversitesi Fen Bilimleri Dergisi / 2024 / Volume: 13 Issue: 4

SimCLR-based Self-Supervised Learning Approach for Limited Brain MRI and Unlabeled Images

Kazım Fırıldak(FIRAT ÜNİVERSİTESİ)0000-0002-1958-3627
Gaffari Çelik(AGRI IBRAHIM CECEN UNIVERSITY, VOCATIONAL SCHOOL)0000-0001-5658-9529
Muhammed Fatih Talu(INONU UNIVERSITY, FACULTY OF ENGINEERING)0000-0003-1166-8404
Pages
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Publication Date
2024-12-31
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

As a result, the model demonstrated superior performance compared to other models, with high performance values such as 99.35% c_acc and 96.31% p_acc.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this study, a SimCLR-based model is proposed for the classification of unlabeled brain tumor images in medical imaging using a self-supervised learning (SSL) technique.
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Key Findings & Data:Additionally, the performances of different SSL techniques (Barlow Twins, NnCLR, and SimCLR) are analyzed to evaluate the performance of the proposed model.
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Conclusion & Impact:As a result, the model demonstrated superior performance compared to other models, with high performance values such as 99.35% c_acc and 96.31% p_acc.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Unlabeled images · Brain tumor · Self-Supervised Learning

Abstract

In this study, a SimCLR-based model is proposed for the classification of unlabeled brain tumor images in medical imaging using a self-supervised learning (SSL) technique. Additionally, the performances of different SSL techniques (Barlow Twins, NnCLR, and SimCLR) are analyzed to evaluate the performance of the proposed model. Three different datasets, consisting of pituitary, meningioma, and glioma brain tumors as well as non-tumor images, were used as the dataset. Out of a total of 7,671 images, 6,128 were used as unlabeled data, and the model was trained with both labeled and unlabeled data. The proposed model achieved high performance with unlabeled data, reducing the need for manual labeling. As a result, the model demonstrated superior performance compared to other models, with high performance values such as 99.35% c_acc and 96.31% p_acc.

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Cite This Article

Fırıldak, K., Çelik, G., Talu, M. F. (2024). SimCLR-based Self-Supervised Learning Approach for Limited Brain MRI and Unlabeled Images. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, Volume: 13 Issue: 4. https://doi.org/10.17798/bitlisfen.1558069

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