MR Görüntülerinden Beyin Tümörünün A-ESA Tabanlı Bir Yaklaşımla Otomatik Sınıflandırılması

Year 2024, Volume: 10 Issue: 2, 325 - 341, 31.12.2024

Elif Yildiz , Fatih Demir , Abdülkadir Şengür

https://doi.org/10.29132/ijpas.1398148

https://izlik.org/JA57RR29LZ

Abstract

Beyin tümörleri dünya çapında önemli bir patolojik durumu temsil etmektedir. Be-yin içindeki dokunun anormal büyümesiyle karakterize edilen bu tümörler, sağlıklı beyin dokularını yerinden ederek ve kafa içi basıncını yükselterek ciddi bir tehdit oluşturmaktadır. Zamanında müdahale edilmediği takdirde bu durumun sonuçları ölümcül olabilir. Manyetik Rezonans Görüntüleme (MRG), özellikle yumuşak do-kuları incelemek için çok uygun olan güvenilir bir tanı yöntemi olarak öne çık-maktadır. Bu makale, Manyetik Rezonans (MR) görüntülerini kullanarak beyin kanserlerinin otomatik tespiti için yenilikçi bir derin öğrenme tabanlı yaklaşım sunmaktadır. Önerilen metodoloji, MR görüntülerinden derin özellikler çıkarmak için yeni bir Residual-ESA modelinin (A-ESA, yani Residual Convolutional Neural Network) sıfırdan eğitilmesini içermektedir. Önerilen yaklaşım, 2 sınıf (sağlıklı ve tümör) ve 4 sınıf (glioma tümörü, meningioma tümörü, hipofiz tümörü ve tümörsüz) veri setlerinden oluşan iki ayrı veri seti üzerinde değerlendirilmiştir. 2 sınıflı ve 4 sınıflı veri kümeleri için en iyi sınıflandırma doğruluğu sırasıyla %88.23 ve %77.14 idi.

Keywords

beyin tümörü , MRG , derin öğrenme , sınıflandırma

Automatic Classification of Brain Tumor from MR Images Using an A-ESA Based Approach

https://izlik.org/JA57RR29LZ

Abstract

Brain tumors represent a significant pathological condition globally. Characterized by the aberrant growth of tissue within the brain, they pose a severe threat by displacing healthy brain tissues and elevating intracranial pressure. Without timely intervention, the implications of this condition can be fatal. Magnetic Resonance Imaging (MRI) stands as a dependable diagnostic modality, particularly well-suited for examining soft tissues. This paper introduces an innovative deep learning-based approach for the automatic detection of brain cancers utilizing Magnetic Resonance (MR) images. The proposed methodology involves the training of a novel Residual-ESA model (A-ESA, i.e., Residual Convolutional Neural Network) from the ground up to extract profound features from MR images.The proposed approach was evaluated on two separate data sets consisting of 2 classes (healthy and tumor) and 4 classes (glioma tumor, menin-gioma tumor, pituitary tumor and tumor-free) data sets. The best classification ac-curacy for the 2-class and 4-class datasets was 88.23% and 77.14%, respectively.

Keywords

brain tumor , MRI , deep learning , classification

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