Histopatolojik Görüntülerde Meme Kanseri Sınıflandırması İçin Maliyet Etkin Yeni Bir CNN Modelinin Geliştirilmesi

Year 2024, Volume: 29 Issue: 3, 896 - 912, 31.12.2024

Karwan Noori Nadr Jaf , Özkan İnik

https://doi.org/10.53433/yyufbed.1498736

Abstract

Kanser, hem gelişmiş sanayileşmiş ülkeleri hem de gelişmekte olan ülkeleri etkileyen, dünya çapında hastalık ve ölümün önde gelen nedenlerinden biridir. Özellikle kadınlar arasında meme kanseri, oldukça yaygın ve ciddi bir kanser türüdür ve bu nedenle tıp alanında geniş çaplı araştırmaların odak noktası olmuştur. Meme kanseri teşhisinde histopatolojik görüntüler, içerdiği değerli fenotipik bilgiler nedeniyle kritik bir rol oynar. Meme histopatolojik görüntü analizinin (BHIA) doğruluğunu ve nesnelliğini artırmak amacıyla, bu görüntüler üzerinde sınıflandırma ve tespit görevleri derin öğrenme mimarisi yaklaşımları kullanılarak gerçekleştirilir. Bu makalede, Meme Kanseri Histopatolojik Veritabanı (BreakHis) kullanılarak yapılan ön deneyde, dört son teknoloji ve özel CNN mimarisi önerilmiştir. Deneysel sonuçlar, önerilen özel modelin 40x ve 200x büyütme faktörlerinde kayda değer bir performans sergilediğini ve sırasıyla %97.49 ve %97.77 doğruluklara ulaştığını, diğer modelleri geride bıraktığını göstermektedir. ResNet-50 modeli ise 100x ve 400x büyütme faktörlerinde daha yüksek doğruluk elde etmiş ve sırasıyla %98.56 ve %96.43 doğruluk oranlarına ulaşmıştır. Diğer son teknoloji modellerle karşılaştırıldığında, önerilen CNN modeli sadece çok daha kısa bir süre içinde verimli eğitim göstermekle kalmamış, aynı zamanda daha az katman sayısı ile üstün hesaplama verimliliğine sahip olduğunu göstermiştir. Parametre sayısı bir modelden daha yüksek olmasına rağmen, model hesaplama verimliliği ile model kapasitesi arasında olumlu bir denge kurmaktadır. Elde edilen sonuçlar ve mevcut literatür ışığında, gelecekteki çalışmalar, meme kanseri sınıflandırmasında performans değerlerini artırmak amacıyla daha da geliştirilebilir.

Keywords

CNN, Derin öğrenme, Histopatoloji Görüntüleri, Meme kanseri, Sınıflandırma

Development of a Cost-Effective Novel CNN Model for Breast Cancer Classification in Histopathological Images

Abstract

Cancer, is a leading cause of disease and death worldwide, affecting both advanced industrialized and developing nations. Breast cancer, specifically among women, is a highly prevalent and serious type of cancer, making it a focal point for extensive research in the field of medicine. In the diagnosis of breast cancer, histopathological images play a crucial role because of the abundance of valuable phenotypic information they contain. To enhance the accuracy and objectivity of breast histopathological image analysis (BHIA), classification, and detection tasks are performed on these images using deep learning architecture approaches. In a preliminary experiment conducted in this paper using the Breast Cancer Histopathological Database (BreakHis), four state-of-the-art and custom CNN architectures were proposed. The experimental results demonstrate the notable performance of the proposed custom model at 40x and 200x magnification factors, reaching accuracies of 97.49% and 97.77%, surpassing other models. The ResNet-50 model achieved higher accuracy at 100x and 400x magnifications, with accuracies of 98.56% and 96.43%, respectively. Compared to other state-of-the-art models, the proposed CNN model not only shows efficient training with a significantly shorter timeframe but also features a reduced number of layers, highlighting its superior computational efficiency. Although the parameter count is higher than that of one of the models, the model strikes a favorable balance between computational efficiency and model capacity. In light of the achieved outcomes and the existing literature, forthcoming studies endeavor can be pursued further to enhance the performance values in breast cancer classification.

Keywords

Breast cancer, Classification, CNN, Deep learning, Histopathology Images

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