Beyin Bilgisayarlı Tomografi Görüntülerinde Yapay Zeka Tabanlı Beyin Damar Hastalıkları Tespiti

Öz

Serebrovasküler hastalık (SVH), beyin damarlarının tıkanması veya kanaması nedeniyle insanlarda felce ve hatta ölüme neden olmaktadır. SVH tipinin uzman tarafından erken teşhisiyle olumsuz etkiler doğru bir tedavi süreci ile engellenebilir. Ancak, hastanelerde veya acil servislerde yeterli sayıda uzmanın görevlendirilmesi her zaman mümkün olmamaktadır. Bu nedenle, bu çalışmada, tanı sürecini hızlandırmak ve uzmanların yükünü hafifletmek için beyin bilgisayarlı tomografi görüntülerinden SVH tespiti için yapay zeka tabanlı bir klinik karar destek sistemi önerilmiştir. Yapay zekanın bir alt kümesi olan derin ögrenme modeli, SVH’nin önce tespit edildiği ve ardından iskemik veya hemorajik olarak sınıflandırıldığı iki aşamalı bir süreçle uygulanmıştır. Ayrıca geliştirilen sistem, SVH teşhisi için kullanıcı dostu bir arayüz sunan özel olarak tasarlanmış¸ masaüstü uygulamamıza entegre edilmiştir. Deneysel sonuçlar, sistemimizin uzmanlar için erken teşhis ve tedaviyi geliştirme konusunda büyük bir potansiyele sahip olduğunu ve hastaların iyileşme oranına katkıda bulunacağını göstermektedir.

Anahtar Kelimeler

• Yapay Zeka
• Derin Öğrenme
• Serebrovasküler Hastalık
• Evrişimsel Sinir Ağları
• Görüntü İşleme

Artificial Intelligence-based Cerebrovascular Disease Detection on Brain Computed Tomography Images

Abstract

Cerebrovascular disease (CVD) causes paralysis and even mortality in humans due to blockage or bleeding of brain vessels. The early diagnosis of the CVD type by the specialist can avoid these casualties with a correct course of treatment. However, it is not always possible to recruit enough specialists in hospitals or emergency services. Therefore, in this study, an artificial intelligence (AI)-based clinical decision support system for CVD detection from brain computed tomography (CT) images is proposed to improve the diagnostic results and relieve the burden of specialists. The deep learning model, a subset of AI, was implemented through a two-step process in which CVD is first detected and then classified as ischemic or hemorrhagic. Moreover, the developed system is integrated into our custom-designed desktop application that offers a user-friendly interface for CVD diagnosis. Experimental results prove that our system has great potential to improve early diagnosis and treatment for specialists, which contributes to the recovery rate of patients.

Keywords

• Artificial Intelligence
• Deep Learning
• Cerebrovascular Disease
• Convolutional Neural Network
• Image Processing
• Artificial Intelligence, Deep Learning, Cerebrovascular Disease, Convolutional Neural Network, Image Processing

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