Brain Tumor Detection via Explainable Convolutional Neural Networks

Abstract

Especially since the early 2000s, deep learning techniques have been known as the most important actors of the field of artificial intelligence. Although these techniques are widely used in many different areas, their successful performance in the field of healthcare attracts more attention. However, the situation that these techniques are optimized with much more parameters than traditional machine learning techniques causes complex solution processes and they become opaque against human-sided perception level. For this reason, alternative studies have been carried out in order to make such black-box intelligent systems consisting of deep learning techniques reliable and understandable in terms of their limitations or error-making tendencies. As a result of the developments, the solutions that led to the introduce of a sub-field called as explainable artificial intelligence allow understanding whether the solutions offered by deep learning techniques are safe. In this study, a Convolutional Neural Networks (CNN) model was used for brain tumor detection and the safety level of that model could be understood through an explanatory module supported by the Class Activation Mapping (CAM). For the application process on the target data set, the developed CNN-CAM system achieved an average accuracy of 96.53%, sensitivity of 96.10% and specificity of 95.72%. Also, feedback provided by the doctors regarding the CAM visuals and the overall system performance showed that the CNN-CAM based solution was accepted positively. These findings reveal that the CNN-CAM system is reliable and understandable in terms of tumor detection.

Keywords

• convolutional neural networks
• explainable artificial intelligence
• brain tumor
• deep learning

Açıklanabilir Evrişimsel Sinir Ağları ile Beyin Tümörü Tespiti

Abstract

Derin öğrenme teknikleri özellikle 2000’li yılların başından bu yana yapay zeka alanının en önemli temsilcileri olarak bilinmektedir. Bu teknikler birçok farklı alanda yaygın bir biçimde kullanılıyor olsa da özellikle sağlık alanındaki başarılı performansları dikkatleri daha çok çekmektedir. Ancak bu tekniklerin geleneksel makine öğrenmesi tekniklerine göre çok daha fazla sayıda parametrelerle optimize ediliyor olması, çözüm süreçlerinin karmaşık olmasına ve insan taraflı algı düzeyine kapalı olmalarına sebep olmaktadır. Bu sebeple kara-kutu olarak da adlandırılan derin öğrenme tekniklerden oluşan zeki sistemleri insan gözünde güvenilir yapmak ve söz konusu sistemlerin sınırlılıklarını ya da hata yapma eğilimlerini anlayabilmek adına alternatif çalışmalar gerçekleştirilmeye başlanmıştır. Gelişmeler neticesinde açıklanabilir yapay zeka olarak adlandırılan bir alt-alanın doğmasına yol açan çözümler, derin öğrenme tekniklerinin sunduğu çözümlerin güvenli olup olmadığının anlaşılmasına olanak sağlamaktadır. Bu çalışmada, beyin tümörü tespiti için bir Evrişimsel Sinir Ağları (ESA) modeli kullanılmış ve modelin güvenlik düzeyi, Sınıf Aktivasyon Haritalama (SAH / CAM: Class Activation Mapping) destekli açıklanabilir bir modül üzerinden anlaşılabilmiştir. Geliştirilen ESA-SAH sistemi, hedef veri seti üzerindeki uygulama sürecinde ortalama %96,53 doğruluk, %96,10 duyarlılık ve %95,72 özgüllük sağlamıştır. Yine doktorların sistemdeki SAH görsellerine ve genel sistem performansına yönelik sundukları dönütler de ESA-SAH tabanlı çözümün pozitif yönde kabul edildiğini göstermiştir. Bu bulgular, ESA-SAH sisteminin tümör tespitinde güvenilir ve anlaşılır olduğunu ortaya koymaktadır.

Keywords

• Evrişimsel sinir ağları
• açıklanabilir yapay zeka
• beyin tümörü
• derin öğrenme

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