İnmenin Beyin Radyolojik BT Görüntülerinden Bilgisayar Destekli Derin Öğrenmeye Dayalı Değerlendirilmesi

Çalışmanın amacı, MATLAB 2019b arayüzünde Derin Öğrenme modelleri ile inme hastalarının beyin BT'lerinden Görüntü İşleme kullanarak anormal alan(lar)ı tespit etmek ve hastalarda beyin dokularındaki inme değişikliklerini doğru bir şekilde değerlendirmektir. TOBB ETÜ ve Yıldırım Beyazıt Üniversitesi Hastanelerinden 25-75 yaş aralığında 1000 hasta (500 inme şüphelisi, 500 sağlıklı katılımcı) etik kurul sertifikasına göre seçilmiştir. Bu çalışma için hastaların görüntü verilerinden doğruluğu artırmak ve fazlalığı ortadan kaldırmak için sadece lateral ve 4. ventrikül BT görüntüleri kullanıldı. İlk olarak bu görüntüler Görüntü İşleme yöntemleri (Görüntü Toplama, Ön İşleme, Eşikleme, Segmentasyon, Morfolojik İşlemler vb.) ile işlenmiştir. Bu yöntemlerden sonra elde edilen lateral ventrikül görüntüsü 6 spesifik alana bölündü ve 4. ventrikül görüntüsü otomatik bilgisayarlı Alberta Stroke Skorlama gibi sırasıyla 14 spesifik alana bölündü. 1000 görüntü için, belirli sınıf adlarıyla (sağlıklı ve felçli olarak) toplam 20x1000=20000 adet BT alt görüntüsü elde edilmiş ve Yapay Zeka (AI) ve Derin Öğrenme (DL) modellerinin (Levenberg ile optimize edilmiş YSA) girdisi olarak kullanılmıştır. Marquardt yöntemi ve KSA). Bu yaklaşım, doktorlara sonuçlarını bir karar destek sistemi ile desteklemeleri, teşhis süresini hızlandırmaları ve olası yanlış teşhis oranlarını azaltmaları için önemli bir şans verebilir.

Anahtar Kelimeler:

Yapay Zeka, Derin Öğrenme, İnme Tanılama, Bilgisayar Destekli Tanı, İnme Önleme

Computer Aided Deep Learning Based Assessment of Stroke From Brain Radiological CT Images

The aim of the study is to detect the abnormal area(s) from brain CTs of stroke patients using Image Processing and to accurately evaluate the stroke changes in brain tissues among patients with Deep Learning models in MATLAB 2019b interface. 1000 patients (500 stroke suspected, 500 healthy participants) were chosen between 25 and 75 age ranges from TOBB ETU and Yıldırım Beyazıt University Hospitals according to the ethics committee certificate. For this study, for increasing the accuracy and eliminating the redundancy, from the image data of the patients, only lateral and 4th ventricle CT images were used. Firstly, these images were processed via Image Processing methods (Image Acquisition, Preprocessing, Thresholding, Segmentation, Morphological Operations etc.). After these methods, the resulted lateral ventricle image was split into 6 specific areas and 4th ventricle image was split into 14 specific areas like automated computerized Alberta Stroke Scoring, respectively. For 1000 images, totally 20x1000=20000 pieces of CT subimages were obtained with the specific class names (as healthy and stroke) and were used as the input of Artificial Intelligence (AI) and Deep Learning (DL) models (optimized ANN with Levenberg-Marquardt method and CNN). This approach can give an important chance to the doctors for supporting their results with a decision support system, speeding up the diagnosis time and also decreasing the possible rate of misdiagnosis.

Keywords:

Artificial Intelligence, Deep Learning, Stroke Diagnosis, CAD, Stroke Prevention,

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