A Deep Feature Based Decision Support System for Breast Cancer Diagnosis

Yıl 2019, Cilt: 7 Sayı: 1, 213 - 227, 01.03.2019

Nuh Alpaslan

https://doi.org/10.15317/Scitech.2019.193

Öz

The breast cancer is the second leading cause of cancer deaths among women
after lung cancer.  Early diagnosis is
quite significant with breast cancer treatment. Mammography is the most
commonly used imaging technique for the early detection of breast cancer.
Researches have been shown that the mortality rate can decrease by 30% for
women who have mammogram regularly over 50 years of age (Jadoon et al. 2017). However, interpreting mammograms is often subjective.

In this study, an integrated system for automated detection,
classification, and content based retrieval of breast masses is presented.  In this manner, physician’s decisions on mass
were expressed by high-level deep features and low-level feature set. In
proposed framework, to extract low-level features, a graph based visual
saliency (GBVS) method is used for mass detection and the nonsubsampled
contourlet transform (NSCT) and eig(Hess)-HOG are used for feature extraction.
In addition, high-level convolutional neural network features have been used.
Then, two extreme learning machine (ELM) classifiers rely on the features
mentioned above is employed to predict category of test images. And outputs of
classifiers based on each feature were examined together to define the kind of
test image. The image retrieval and classification performances are evaluated
and compared on IRMA mammographic dataset by using both the precision-recall
(PR) and classification accuracies. Experimental results demonstrate the
effectiveness of the proposed system and the viability of a real-time clinical
application.

Anahtar Kelimeler

Breast cancer, Mammogram, Classification, Computer-aided diagnosis (CAD), Content-based image retrieval

MEME KANSERİ TANISI İÇİN DERİN ÖZNİTELİK TABANLI KARAR DESTEK SİSTEMİ

Öz

Meme kanseri, akciğer kanserinden sonra kadınlarda kanser ölümlerinin
ikinci önemli sebebidir. Erken tanı, meme kanseri tedavisinde oldukça
önemlidir. Mamografi, meme kanserinin erken teşhisinde en çok kullanılan
görüntüleme tekniğidir. Yapılan araştırmalar, 50 yaşın üstünde düzenli
mamografi çektirmenin kadınlar için ölüm oranını %30 oranında azaltabileceğini
göstermektedir. Ancak, mamogramların yorumlanması genellikle özneldir.

Bu
çalışmada, göğüs kitlelerinin otomatik tespiti, sınıflandırılması ve içerik
tabanlı erişimi için entegre bir sistem sunulmuştur. Bu kapsamda, hekimlerin
kitle hakkındaki kararları, üst düzey derin öznitelikler ve düşük seviye
öznitelik seti ile ifade edilmiştir. Önerilen sistemde düşük seviyeli
öznitelikleri elde etmek için, kitle tespitinde graf tabanlı görsel çıkıntı
yöntemi kullanılmış ve öznitelik çıkarımı için örneklemesiz contourlet dönüşümü
ve eig(Hess)-HOG yöntemleri kullanılmıştır. Ayrıca, yüksek seviyeli evrişimsel
sinir ağı öznitelikleri kullanılmıştır. Ardından, test görüntülerinin
kategorisini tahmin etmek için yukarıda bahsedilen özniteliklere dayalı iki
aşırı öğrenme makinesi (AÖM) sınıflandırıcısı kullanılmıştır. Farklı
özniteliklere dayalı sınıflandırıcıların sonuçları, test görüntülerinin türünü
belirlemek için analiz edilmiştir. Görüntü erişimi ve sınıflandırma
performansları, hem kesinlik-duyarlılık hem de sınıflandırma doğrulukları
kullanarak IRMA mammographic patches veri setinde değerlendirilip ve
karşılaştırılmıştır. Deneysel sonuçlar, önerilen sistemin etkililiğini ve
gerçek zamanlı klinik uygulamalardaki kullanılabilirliğini göstermektedir.

Anahtar Kelimeler

Meme Kanseri, Mamogram, Sınıflandırma, Bilgisayar Destekli Tanı, İçerik tabanlı görüntü erişimi

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