Sema Nizam Abdulghani, Ahmed Freidoon Fadhi, Seyfettin Sinan Gültekin

Meme Kanseri Tespiti İçin Destek Vektör Makinesi İle Alexnet Kullanarak Transfer Öğrenimi

Meme kanseri, şu anda dünya çapında kadın ölümlerinin önde gelen nedenlerinden biridir. Meme kanseri teşhisi için bilgisayar destekli teşhis sistemleri geliştirmek, son yıllarda birçok araştırmacı için ilgi çekici bir sorun haline geldi. Araştırmacılar, büyük bir başarı elde eden Evrişimli Sinir Ağları (CNN'ler) dahil olmak üzere sınıflandırma problemleri için derin Öğrenme tekniklerine odaklandılar. CNN'ler, özellikle biyomedikal görüntü işleme görevlerinde deneysel başarılar elde eden, araştırma topluluğu ve endüstriden dikkat çeken özel bir derin, ileri beslemeli ağ türüdür. Bu çalışmada, meme kanseri histopatolojik görüntülerini kamuya açık (BreakHis veri seti) sınıflandırmak için önceden eğitilmiş bir CNN modelini uyarlayan transfer öğrenme ve derin özellik çıkarma yöntemleri kullanılmıştır. AlexNet modeli bu çalışmada yama stratejisi ile ele alındı, ve daha fazla ince ayar için önceden eğitilmiş AlexNet kullanıldı. Elde edilen özellikler daha sonra destek vektör makineleri (SVM) kullanılarak sınıflandırıldı. Değerlendirme sonuçları, SVM sınıflandırıcısı ile önceden eğitilmiş Alexnet'in, farklı büyütme faktörleri için beş kat çapraz doğrulama tekniği kullanarak 92 % ile 96 % arasında bir doğruluk sağladığını göstermektedir.

Transfer Learning using Alexnet with Support Vector Machine for Breast Cancer Detection

Breast cancer is one of the leading causes of women death worldwide currently. Developing a computer-aided diagnosis system for breast cancer detection became an interesting problem for many researchers in recent years. Researchers focused on deep learning techniques for classification problems, including Convolutional Neural Networks (CNNs), which achieved great success. CNN is a specific class of deep, feedforward network that has obtained attention from the research community and achieved great successes, especially in biomedical image processing. In this paper, deep feature extraction methods are used which with pre-trained CNN model to classify breast cancer histopathological images from the publically available (BreakHis dataset). The data set includes two classes, benign and malignant, with four different magnification factors. A patch strategy method proposed based on the extraction of image patches for training the CNN and the combination of these patches for classification. AlexNet model is considered in this work with patch strategy, and pre-trained AlexNet is used for fine-tuning the system. Then, the Support Vector Machine (SVM) was used to classify the obtained features.The evaluation results show that the pre-trained Alexnet with SVM classification and patch strategy yields the best accuracy. Accuracy between 92% and 96% was achieved using five-fold cross-validation technique for different magnification factors.

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