Meme Kanseri Tümörlerinin Derin Öğrenme Algoritmaları ile Sınıflandırılması

Abstract

Meme kanseri, kadınlarda ölümlere neden olabilen hastalıklar arasında en başlarda gelen hastalıklardan biridir. Yapılan araştırmalara göre meme kanserinin erken teşhisi ile ölüm oranları düşürülebilmektedir. Meme kanserinin teşhisinde incelenen mamogram görüntülerinin radyologlar tarafından incelenmesi uzun zaman almakta hatta zaman zaman bu incelemelerde hatalı sonuçlar elde edilebilmektedir. Meme kanserinin erken aşamalarda teşhis edilebilmesi için yapay zekâ yöntemleri kullanılarak yapılan çalışmalar oldukça önemlidir. Gelişen teknolojiyle birlikte birçok farklı derin öğrenme modeli bu hastalığın teşhisinde kullanılmaktadır. Bu çalışmada, meme kanserinin teşhisi için Inception-ResNet-V2 derin öğrenme modeli önerilmektedir. Önerilen derin öğrenme modeli, Inception ve ResNet modellerinin melezi bir mimari olup etkili bir şekilde geliştirilmiş sınıflandırma ve tanıma performansına sahiptir. Önerilen derin öğrenme mimarisi sırasıyla önişleme, sınıflandırma ve performans değerlendirme olmak üzere üç aşamadan oluşmaktadır. Önerilen model ile %96.21 doğruluk, %97.48 geri çağırma, %98.18 kesinlik, %97.83 F-ölçütü, %98.00 eğri altında kalan alan ve 0.83 cohen kappa performans değerleri elde edilmiştir. Elde edilen sonuçlar, çalışmada kıyaslama aşamasında kullanılan diğer derin öğrenme mimarilerinden elde edilen sonuçlar ile karşılaştırıldığında önerilen modelin meme kanseri teşhisinde daha iyi performans sergilediğini kanıtlamaktadır.

Keywords

• Meme Kanseri
• Mamogram
• Derin Öğrenme
• Sınıflandırma
• Inception- ResNet-V2

Classification of Breast Cancer Tumors with Deep Learning Algorithms

Abstract

Breast cancer is one of the leading diseases among diseases that can cause death in women. Studies have shown that early detection of breast cancer can reduce mortality rates. The mammogram images used in the diagnosis of breast cancer are examined by radiologists. Investigations take a long time and sometimes erroneous results can be obtained. Studies using artificial intelligence methods are very important for the diagnosis of breast cancer in the early stages. It has also been observed that the diagnoses obtained with mammogram images are better than the diagnosis of medical experts. With the developing technology, many different deep learning models are used in the diagnosis of the disease. In this study, the Inception-ResNet-V2 deep learning model is proposed for the diagnosis of breast cancer. The proposed deep learning model is a hybrid architecture of Inception and ResNet models and has effectively improved classification and diagnosis performance. The proposed deep learning architecture consists of three stages, namely preprocessing, classification, and performance evaluation, respectively. With the proposed model, 96.21% accuracy, 97.48% recall, 98.18% precision, 97.83% F-score, 98.00% area under the curve, and 0.83 cohen kappa performance values were obtained. The obtained results prove that the proposed model performs better in breast cancer diagnosis when compared with the results obtained from other deep learning architectures used in the benchmarking stage in the study.

Keywords

• Breast Cancer
• Mammogram
• Deep Learning
• Classification
• Inception-ResNet-V2

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