Yeni ve etkili bir evrişimsel sinir ağı tabanlı model kullanılarak beyin tümörü görüntülerinin çoklu sınıflandırılması

Öz

Konvansiyonel yöntemlerde, manyetik rezonans görüntüleme (MRG) kullanılarak beyin görüntülerinden tümör hastalığının tespiti, uzman bir tıp doktoru gerektiren zor ve insan hatasına açık bir çalışma alanıdır. Beyin tümörlerinin eksik veya yanlış tespiti, insan ömrünün kısalması gibi önemli istenmeyen sonuçlara yol açabilir. Bu zorlukların üstesinden gelmek için birçok araştırmacı yapay zeka destekli otonom hastalık tespiti üzerinde çalışmaktadır. Bu çalışmanın amacı, hızlı ve güvenilir otonom kanser tespiti için derin öğrenme mimarileriyle beyin MR görüntülerini kullanmaktır. Bu çalışmada, kamuya açık literatürde yaygın olarak kullanılan iki farklı veri seti ve bir evrişimli sinir ağı (CNN) altyapısı kullanılarak beyin MR görüntüleri sınıflandırılmıştır. Eğitim, doğrulama ve test süreçlerinde benzer parametrelerle yapılan deneyler sonucunda elde edilen sonuçlar, literatürdeki diğer çalışmalarla ayrıntılı olarak karşılaştırılmış ve aralarındaki farklar ortaya konulmuştur. Çalışmada önerilen yeni CNN tabanlı model, doğruluk değerlendirme metriğinde %99,76 sınıflandırma sonucuna ulaşmıştır. Elde edilen sonuçlar çalışmada önerilen modelin beyin tümörü tespitinde yüksek doğrulukla kullanılabileceğini ve diğer çalışma alanlarına ışık tutabileceğini göstermiştir.

Anahtar Kelimeler

• Beyin tümörü
• sınıflandırma
• evrişimli sinir ağı-CNN
• derin öğrenme-DL
• görüntü işleme

Multiple classification of brain tumor images using a new and efficient convolutional neural network-based model

Abstract

In conventional methods, the detection of tumour disease from brain images using magnetic resonance imaging is a difficult and human error-prone field of study that requires an expert medical doctor. Incomplete or inaccurate detection of brain tumours can have significant undesirable consequences such as shortening of human life. In order to overcome these difficulties, many researchers are working on autonomous disease detection supported by artificial intelligence. The aim of this study is to utilise brain magnetic resonance images with deep learning architectures for fast and reliable autonomous cancer detection. In this study, brain images are classified using two different datasets and a convolutional neural network infrastructure, which are widely used in the publicly available literature. The results obtained as a result of experiments with similar parameters in training, validation and testing processes are compared in detail with other studies in the literature and the differences between them are presented. The new convolutional neural network-based model proposed in the study achieved 99.76% classification result in the accuracy evaluation metric. The results obtained showed that the model proposed in the study can be used with high accuracy in brain tumour detection and can shed light on other fields of study.

Keywords

• Brain tumor
• classification
• convolutional neural network-CNN
• deep learning-DL
• image processing

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