Beyin Bilgisayarlı Tomografi Anjiyografi Tarama Görüntülerinde Derin Öğrenme Tabanlı Otomatik Serebral Anevrizma Tespiti

Yıl 2024, ERKEN GÖRÜNÜM, 1 - 1

Meltem Yavuz Çelikdemir , Ayhan Akbal

https://doi.org/10.2339/politeknik.1261854

Öz

Serebral anevrizmalar, insan hayatını tehdit eden önemli bir hastalıktır. Bu anevrizmaların rüptüre olması beyin arterlerinde kanamalara sebep olmaktadır. Klinik olarak cerebral anevrizmaların tanısında bilgisayarlı tomografi anjiyografi yaygın olarak kullanılmaktadır. Bununla birlikte radyoloji uzmanlarının tomografi görüntülerini yorumlama hataları hastalar için hayati öneme sahiptir. Bu öneme binaen yapılan derin öğrenme tabanlı çalışmalar bu hataları minimum seviyede tutmaya yardımcı olmayı amaçlamaktadır. Bu amaç doğrultusunda yapılan bu çalışmada serebral anevrizmaların tespiti için BTA görüntüleri kullanılmıştır. BTA görüntü analizleri için ise Evrişimli Sinir Ağı aracılığı ile derin öğrenme metodolojisi tercih edilmiştir. Derin öğrenme sonucunda elde edilen eğitimin doğrulama doğruluğu %99.54, duyarlılığı %98, özgüllüğü %100, hassaslığı %100 ile yüksek orana sahiptir. Eğitim veri seti olarak, anevrizmalı hasta görüntüleri için 127 doğru pozitif, 1 yanlış negatif, anevrizmasız hasta görüntüleri için 89 doğru pozitif, 0 yanlış pozitif olarak sonuç vermiştir. Eğitilmiş ağda, harici test için 75 CTA görüntüde %86.6 gibi yüksek bir doğrulukla sonuçlar elde edildi. Test işleminde anevrizma tespit edilen bir görüntü için bölgesel boyut analizi yapılmıştır.

Anahtar Kelimeler

Derin öğrenme, BTA-tarama görüntüsü, serebnal anevrizma

A Deep Learning Based on Automatic Cerebral Aneurysm Detection in Brain Computed Tomography Angiography Scan Images

Öz

Cerebral aneurysms are an important disease that threatens human life. Rupture of these aneurysms causes hemorrhages in the cerebral arteries. Computed Tomography Angiography (CTA) is widely used in the clinical diagnosis of cerebral aneurysms. Interpretation errors by radiologists in examining these Computed Tomography (CT) images are vital for patients. Based on this importance, deep learning-based studies aim to help keep these errors to a minimum. For this purpose, CTA images were used to detect cerebral aneurysms in this study. For CTA image analysis, deep learning methodology was preferred through Convolutional Neural Network (CNN). The validation accuracy of the training obtained as a result of deep learningg has a high rate of validation with 99.54% accuracy, 100% sensitivity, 98.89% specificity and 100% precision. As a training dataset, it yielded 127 true positives and 1 false positive for patient images with aneurysm, 89 true negatives and 0 false negative for images of patients with non-aneurysms. In this trained network, results were obtained with a high accuracy of 86.6% on 75 CTA images for external test. Regional dimension analysis was also performed for an image with an aneurysm detected in the test process.

Anahtar Kelimeler

Deep Learning, CTA-Scan Image, cerebnal aneurysm

Kaynakça

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