Meme Kanseri Sınıflandırması İçin Yeni Bir Yöntem: Ultrason Görüntülerinde Sinyal İşleme Temelli Bir Yaklaşım

Year 2023, , 299 - 306, 31.12.2023

Şerife Gengeç Benli , Zeynep Ak

https://doi.org/10.54365/adyumbd.1378982

Abstract

Meme kanseri, dünya genelinde kadınlar arasında ölümün önde gelen nedenlerinden biri olup, doğru ve etkili tanı yöntemlerinin önemi vurgulanmaktadır. Bu çalışma, özellikle meme ultrason görüntülerini kullanarak meme kanseri sınıflandırması alanındaki literatüre yeni bir sinyal işleme yaklaşımı kullanan yöntem ile katkı sağlamaktadır. Çalışma, meme ultrason görüntülerinden elde edilen sinyaller ve Varyasyonel Kip Ayrışımı (VMD) alt bantlarından elde edilen sinyalleri kullanan yeni bir yaklaşım sunmaktadır. Elde edilen sonuçlar ile hem orijinal veriden hem de VMD alt bant sinyallerinden elde edilen özelliklerin iyi huylu ve kötü huylu meme ultrason görüntülerini etkili bir şekilde ayırt edilebileceği gösterilmiştir. Kullanılan algoritma ve uygulanan verilere göre elde edilen sınıflandırma performansları değişmektedir. Sayısal sonuçlara göre, dengelenmiş veriler kullanılarak yapay sinir ağları yöntemi ile yapılan çalışma sonucunda en yüksek sınıflandırma performansı elde edilmiş olup, eğri altında kalan alan değeri 0.9971 ve doğruluk değeri 0.9821 olarak elde edilmiştir.

Keywords

Meme ultrason görüntüleri, Varyasyonel kip ayrışımı, Altbandlar, Sınıflandırma.

A Novel Method for Breast Cancer Classification: A Signal Processing-Based Approach in Ultrasound Images

Abstract

Breast cancer, a leading cause of mortality among women worldwide, the importance of accurate and efficient diagnostic methods is emphasized. This study contributes to the literature on breast cancer classification, particularly using breast ultrasound images, with a new method using a signal processing approach. It introduces a novel approach by combining features extracted from signals obtained from breast ultrasound images with signals from Variational Mode Decomposition (VMD) sub-bands. The results demonstrate that utilizing features from both preprocessed raw data and VMD sub-band signals can effectively distinguish benign and malignant breast ultrasound images. Classification performance varied depending on the algorithms and data used. According to the numerical results, the highest classification performance was achieved through the study with balanced data using the artificial neural network method, with an area under the curve value of 0.9971 and an accuracy value of 0.9821.

Keywords

Breast ultrasound images, Variational mode decomposition, Classification., Sub-bands

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