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Meme kanseri tüm dünyada yaygın bir hastalık olması sebebiyle hastalığın erken teşhisi, hastaların bu hastalıktan tamamen kurtulabilmeleri açısından kritik öneme sahiptir. Hastalığın teşhisini kolaylaştırmak için tıp doktorları bilgisayar destekli uzman sistemlerden yararlanabilmektedir. Bu çalışmada meme kanseri veri örneklerini iyi huylu veya kötü huylu sınıflarına ayırmak için genel regresyon sinir ağı (Generalized Regression Neural Network-GRNN) ve ileri beslemeli sinir ağı (Feed Forward Neural Network-FFNN) temelli bir skor füzyon yöntemi önerilmiştir. Önerilen yöntem Wisconsin Teşhis Meme Kanseri (Wisconsin Diagnostic Breast Cancer-WDBC) veri seti üzerinde test edilmiştir. Bu iki temel ağın ve önerilen yöntemin kullanışlılığı incelenmiş ve performans sonuçları karşılaştırmalı olarak sunulmuştur. Önerilen yöntem sınıflandırma doğruluğu bakımından literatürde WDBC veri setini kullanarak yapılan mevcut çalışmalar ile kıyaslanmıştır. Elde edilen deneysel sonuçlar önerilen yöntemin, meme kanseri teşhisi için umut vadettiğini ve tıp uzmanlarının hastalığa ilişkin karar vermelerinde yardımcı bir araç olarak kullanılabileceğini göstermektedir.
Because breast cancer is a widespread disease worldwide, early diagnosis of the disease is critical for patients to be able to get rid of this disease completely. To facilitate the diagnosis of the disease, doctors can take advantage of computer-backed specialized systems. This study suggested a score fusion method based on the General Regression Neural Network (GRNN) and the Feed Forward Neural Network (FFNN) to divide breast cancer data samples into beneficial or malignant classes. The recommended method was tested on the Wisconsin Diagnostic Breast Cancer (WDBC) data set. The usefulness of these two basic networks and the recommended method has been studied and the performance results have been presented comparatively. The recommended method is compared with existing studies performed using the WDBC data set in literature in terms of classification accuracy. Obtained experimental findings show that the recommended method promises hope for breast cancer diagnosis and can be used as a tool for medical professionals to make decisions about the disease.
Early diagnosis of breast cancer disease is critical for patients to recover from this disease entirely as it is a common disease all over the world. In order to facilitate the diagnosis of the disease, medical doctors can benefit from computer-aided expert systems. In this paper, a score fusion method based on generalized regression neural network (GRNN) and feed forward neural network (FFNN) has been proposed so as to split breast cancer data samples into benign or malignant classes. The proposed method is tested on the Wisconsin Diagnostic Breast Cancer (WDBC) dataset. The utilities of these basic neural networks and the proposed method are examined and comparative performance results are presented. The experimental results show that the proposed method is promising for the diagnosis of breast cancer and may be used as an assisting tool in the decision-making of medical professionals.
Field : Fen Bilimleri ve Matematik
Journal Type : Ulusal
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