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Journals / El-Cezeri / 2022 / Cilt: 9 - Sayı: 4

Brain Tumor Segmentation with Convolutional Neural Networks

Evrişimli Sinir Ağlarında Beyin Tümörü Segmentasyonu

Emin GÖKÇE
Mehmet Fatih DEMİRAL
Ali Hakan ISIK
Mehmet BİLEN
Journal
El-Cezeri
Pages
1518–1528
Publication Date
2022-12-31
DOI
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AI Summary & Key Takeaways

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

When the findings obtained as a result of the training and testing procedures are examined, it is seen that the trained model has been trained as a model that can successfully mark the entire tumor, tumor nucleus and expanding tumor sites with a similarity ratio of 0.908, 0.807 and 0.877 (BO, Dice Coefficient Score), respectively.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Brain tumors are the general name for abnormal cell and mass growth in the skull.
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Key Findings & Data:In recent years, thanks to the rapidly developing deep learning technologies and innovations in the field of medicine, various studies are being conducted to diagnose diseases early and accurately.
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Conclusion & Impact:When the findings obtained as a result of the training and testing procedures are examined, it is seen that the trained model has been trained as a model that can successfully mark the entire tumor, tumor nucleus and expanding tumor sites with a similarity ratio of 0.908, 0.807 and 0.877 (BO, Dice Coefficient Score), respectively.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:U-Net · MRI · Beyin Tümörü

Abstract

Brain tumors are the general name for abnormal cell and mass growth in the skull. In order to diagnose a brain tumor, the most common examination is an MRI (magnetic resonance) image that shows foreign masses in the brain tissue and tissue. After the diagnosis is made, one should quickly plan a course of treatment. After the MRI images are taken, it may take time for the images to be examined and reported by expert radiologists. In recent years, thanks to the rapidly developing deep learning technologies and innovations in the field of medicine, various studies are being conducted to diagnose diseases early and accurately. Minimizing human-caused errors has an important place in these studies. In this study, a new convolutional neural network model has been trained by using artificial intelligence techniques to help experts by marking MRI images. At the training stage, 80% of the BRAST dataset was used by using the U-Net model. 20% of the samples in the dataset were used to evaluate the performance of the model. When the findings obtained as a result of the training and testing procedures are examined, it is seen that the trained model has been trained as a model that can successfully mark the entire tumor, tumor nucleus and expanding tumor sites with a similarity ratio of 0.908, 0.807 and 0.877 (BO, Dice Coefficient Score), respectively.

Özet

Beyin tümörleri kafatası içinde anormal hücre ve kitle büyümesinin genel adıdır. Beyin tümörü tanısı konulabilmesi için en yaygın tetkik beyin dokusu ve dokudaki yabancı kitleleri gösteren MR (manyetik rezonans) görüntülemesinin yapılmasıdır. Tanı konduktan sonra hızlıca tedavi süreci planlamalıdır. MR görüntüleri çekildikten sonra uzman radyologlar tarafından görüntülerin incelenerek raporlanması zaman alabilmektedir. Son yıllarda hızla gelişen derin öğrenme teknolojileri ile tıp alanında bulunan yenilikler sayesinde hastalıkların erken ve doğru teşhis edilmesi için çeşitli çalışmalar yapılmaktadır. İnsan kaynaklı hataların en aza indirilmesi bu çalışmalar içerisinde önemli bir yere sahiptir. Bu çalışmada MRI görüntülerinin işaretlenerek uzmanlara yardımcı olması için yapay zekâ tekniklerinden yararlanılarak yeni bir evrişimli sinir ağı modeli eğitilmiştir. Eğitim aşamasında U-Net modelinden yararlanılarak, BRAST veri kümesinin %80’i kullanılmıştır. Veri kümesi içerisindeki örneklerin %20’si modelin performansının değerlendirilmesi için kullanılmıştır. Eğitim ve test işlemleri sonucunda elde edilen bulgular incelendiğinde eğitilen modelin tüm tümör, tümör çekirdeği ve genişleyen tümör bölgelerini sırayla 0.908, 0.807 ve 0.877 Benzerlik oranı (BO, Dice Coefficent Score) ile başarılı bir şekilde işaretleme yapabilen bir model eğitildiği görülmektedir.

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Cite This Article

GÖKÇE, E., DEMİRAL, M. F., ISIK, A. H., BİLEN, M. (2022). Brain Tumor Segmentation with Convolutional Neural Networks. El-Cezeri, Cilt: 9 - Sayı: 4, 1518–1528. https://ajindex.com/articles/859091

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