Yapay Zeka Tabanlı Doğru Bir Anevrizma Tespit Modeli

Abstract

Serebral anevrizmalar, yaşamı tehdit eden önemli serebrovasküler hastalıklardır ve erken tanı ve tedavi için Bilgisayarlı Tomografi Anjiyografisi’nin (BTA) doğru yorumlanması kritik öneme sahiptir. Bu çalışma, 1211 BTA görüntüsüne 15 farklı Evrişimli Sinir Ağı (ESA) uygulanarak, derin öğrenmenin radyolog kaynaklı yorumlama hatalarını azaltmadaki etkinliğini değerlendirmektedir. Sınıflandırma öncesinde görüntülere çeşitli önişleme ve filtreleme işlemleri uygulanmış ve karşılaştırmalı performans metrikleri elde edilmiştir. En iyi sonuç, literatürde şimdiye kadar bildirilen en yüksek doğruluk oranı olan %99,72 ile yumuşatma filtresi uygulanmış görüntü veri setinde VGG19 mimarisi kullanılarak elde edilmiştir. VGG19 test setinde model çıktıları 272 gerçek negatif (tn), 1 yanlış negatif (fn), 0 yanlış pozitif (fp) ve 90 gerçek pozitiften (tp) oluşmuştur. Bu bulgular, uygun görüntü önişleme ve filtreleme adımlarının, CNN tabanlı anevrizma tespit performansını anlamlı ölçüde artırdığını ve sınıflandırma doğruluğunu iyileştirmede kritik bir rol oynadığını göstermektedir.

Keywords

• Beyin Anevrizması
• BTA-Tarama
• Görüntü Filtreleme
• Yapay Zeka

An Accurate Aneurysm Detection Model based on Artificial Intelligence

Abstract

Cerebral aneurysms are a major, life threatening cerebrovascular disease, and accurate interpretation of Computed Tomography Angiography (CTA) is critical for early diagnosis and treatment. This study evaluates the effectiveness of deep learning in reducing radiologist related interpretation errors by applying 15 different Convolutional Neural Networks (CNNs) to 1,211 CTA images. Prior to classification, images underwent various preprocessing and filtering operations, and comparative performance metrics were obtained. The best result, representing the highest accuracy reported to date of 99.72%, was achieved with a smoothing filtered image dataset using the VGG19 architecture. In the VGG19 test set, model outputs consisted of 272 true negatives (tn), 1 false negative (fn), 0 false positives (fp), and 90 true positives (tp). These findings demonstrate that appropriate image preprocessing and filtering significantly enhance CNN based aneurysm detection performance and play a crucial role in improving classification accuracy.

Keywords

• Artificial Intelligence
• Cerebral Aneurysm
• Image Filtering
• CTA-Scan

Supporting Institution

Fırat University

Ethical Statement

This research was approved on ethical grounds by the Non-Interventional Research Ethics Committee, Firat University Ethics Board, on 17 September 2020 (2020/12–04). “There is no conflict of interest with any person/institution in the article prepared”.

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