Transfer Öğrenme Teknikleri Kullanılarak Kategorik ve İkili Beyin Tümörü Sınıflandırması

Yıl 2023, Cilt: 1 Sayı: 1, 11 - 16, 10.08.2023

Ayşe Gül Eker Gamze Korkmaz Erdem Nevcihan Duru

Öz

Beyin bölgesindeki hücrelerin kontrol dışı çoğalmasıyla oluşan beyin tümörleri yaşam kalitesini ve uzunluğunu etkileyebilir. Yanlış veya geç tanı konulan beyin tümörlü hastaların veya tedavi edilmeyen hastaların hayatta kalma şansı daha düşüktür. MR görüntüleme ekipmanından elde edilen görüntüler tipik olarak beyin kanserlerini teşhis etmek için kullanılır. Artan hasta sayısı ve yüksek doktor yoğunluğu göz önüne alındığında, bilgisayar destekli teknikler özellikle beyin tümörlerinin teşhisinde ve sınıflandırılmasında yardımcı olmaktadır. Bu çalışmada, MRI verilerinden beyin tümörlerini sınıflandırmak için transfer öğrenme teknikleri kullanılmıştır. Çalışmada tümörlü ve tümörsüz ikili veri setine ek olarak glioma, menenjiyom, hipofiz ve tümörsüz görüntülerinden oluşan 4 sınıflı bir veri seti kullanılmıştır. Veri setlerine görüntü ön işleme teknikleri uygulanarak görüntülerdeki tekrarlı bölgeler ve gereksiz bölgeler ortadan kaldırılmıştır. Ardından son katmanı modifiye edilen EfficientNet, XceptionNet ve CoAtNet modelleri ile bu modellerin çok büyük veri setleri (imagenet) üzerinde eğitilmiş ağırlık değerleri kullanılarak sınıflandırma yapılmıştır. Sonuç olarak, CoAtNet'in çoklu sınıflandırma validasyon doğruluğunda (98.26) ve EfficientNet'in ikili sınıflandırmada (99.98) en iyi performansı gösterdiği görülmüştür. Benzer veri setlerine sahip yüksek başarılı çalışmalarla karşılaştırıldığında, başarı metriklerinin bu çalışmalara oldukça yakın olduğu görülmüştür.

Anahtar Kelimeler

transfer öğrenme, coatnet, efficientnet, xceptionnet, sınıflandırma, beyin tümörü

Teşekkür

CAIAC'22 de sunulmuştur. Konferans komitesine teşekkür ederiz.

Categorical and Binary Brain Tumor Classification Using Transfer Learning Techniques

Öz

The quality and length of life may be affected by brain tumors, which are created when cells in the head region proliferate out of control. Patients with misdiagnosed or late-diagnosed brain tumors and untreated patients have a lower chance of survival. Images obtained from MR imaging equipment are typically used to diagnose brain cancers. Given the rising number of patients and the high doctor density, computer-assisted techniques are particularly helpful in the diagnosis and categorization of brain tumors. In this study, transfer learning techniques were used to classify brain tumors from MRI data. In the study, a 4-class dataset made up of glioma, meningioma, pituitary, and no-tumor was used in addition to a binary data set of tumor and no-tumor. Repetitive and unneeded regions in the images were eliminated by applying image preprocessing techniques to the datasets. Following that, classification was performed using the EfficientNet, XceptionNet, and CoAtNet models, which modified the last layer and used the weight values of the models trained on very large datasets (imagenet). As a result, show that CoAtNet performed best in multiclassification validation accuracy (98.26) and EfficientNet in binary classification (99.98). When compared to high-success studies with similar datasets, it was observed that the success metrics were quite close to those of these studies.

Anahtar Kelimeler

transfer learning, coatnet, efficientnet, xceptionnet, classification, brain tumor

Kaynakça

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