ROI görüntülerinde derin inanç ağları kullanarak göğüs kanseri teşhisi

Öz

Elle çıkarılan öznitelikler, görüntü işleme, tanıma ve bilgisayarlı görü için etkili yöntemlerdir. Ancak, veri boyutu ve görüntü çözünürlüklerindeki artış, özniteliklerin elde edilmesinde zorluklara sebep olmuştur. Kararsız, yönteme bağımlı ve hesaplama açısından yoğundurlar. Özellikle, görüntü veri kümelerindeki büyük veriler, öngörülemeyen uzun süreçler doğurur. Görüntü işleme için öznitelik çıkarma algoritmalarının bilgisayar destekli yöntemlere uyarlanması kesin bir ihtiyaçtır. Üretken temsili öğrenme algoritmaları, Derin Öğrenmenin avantajları ile son yıllarda ortaya çıkan yaklaşımlardır. Bu çalışmada, ROI görüntülerinde meme kanseri teşhisi için Derin İnanç Ağlarının (DBN) kullanılmasını önerdim. DBN modelleri, boyutun ROI görüntüleri üzerindeki etkisini değerlendirmek için farklı görüntü boyutları üzerinde tekrarlanmıştır. Önerilen DBN modeli doğruluk, özgüllük, duyarlılık ve kesinlik için sırasıyla %96.32, %96.68, %95.93 ve %96.40 performans oranlarına ulaşmıştır. Sonuç olarak, önerilen ayrıntılı temsili öğrenmeye sahip DBN, üretici yapıların avantajı ile meme kanseri ve sağlıklı dokuların mamogramlarda sınıflandırılması için verimli ve sağlam bir prosedürdür.

Anahtar Kelimeler

• Derin Öğrenme
• Temsili öğrenme
• Derin İnanç ağları
• meme kanseri
• DDSM

Breast cancer diagnosis using deep belief networks on ROI images

Abstract

Hand-crafted features are efficient methods for image processing, recognition, and computer vision. However, the advancements in data size and image resolution lead to inconvenience in feature extraction. Moreover, they are unstable, method-dependent, and computationally intensive due to high dimensions. Especially, big data on image datasets causes unpredictable long process. It is a definite necessity to adjust the feature extraction algorithms to computer-assisted methods for image processing. Generative representational learning algorithms have been emerging approaches with the advantages of Deep Learning. In this study, I proposed employing Deep Belief Networks (DBN) for breast cancer diagnosis on ROI images. DBN models were iterated on different image sizes to evaluate the impact of dimensionality on ROI images. The proposed DBN model has achieved performance rates of 96.32%, 96.68%, 95.93%, and 96.40% for accuracy, specificity, sensitivity, and precision, respectively. Consequently, the proposed DBN with detailed representational learning is an efficient and robust algorithm for the classification of breast cancer and healthy tissues on mammograms by the advantage of generative architectures.

Keywords

• Deep Learning
• representational learning
• Deep Belief networks
• breast cancer
• DDSM

Kaynakça

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