Classification of Breast Lesions via Active Thermograms with the Help of Deep Learning

Year 2023, , 140 - 156, 22.06.2023

Soner Çivilibal , Kerim Kürşat Çevik , Ahmet Bozkurt

https://doi.org/10.29233/sdufeffd.1141226

Abstract

In recent years, artificial intelligence studies, which have developed in parallel with computer hardware, have helped increase patient survival by preventing possible metastasis with early diagnosis of clinicians. There are many studies in literature that carry out diagnosis of cancer in the clinic. In these studies, machine learning and deep learning applications are frequently applied for cancer classification. Similarly, in this study, diagnosis of breast cancer over thermal images with the help of deep learning methods was discussed. The images used in the study were taken open access made available in the DMR-IR dataset. Some preprocessing was done on the images before manual and automatic segmentation methods were applied to segment the breast regions on images. In the manual segmentation process, the mask of the breast regions whose localization information was recorded with VIA was created and the segmentation was performed by subtracting the mask from the original image. In the automatic segmentation process, segmentation was done using Mask R-CNN and U-NET techniques. Segmentation performance analysis was performed for these two methods and classification operations were done with Mask R-CNN, which realized 0.9896 accuracy, 0.9413 Dice and 0.8900 Jaccard. Breast cancer classification was carried out using seven pre-trained architectures (InceptionV3, MobileNet, MobileNetV2, ResNet50, VGG16, VGG19 and Xception) with thermograms consisting of images segmented by manual and Mask-RCNN methods. As a result, MobileNet and InceptionV3 architectures provided the 100% classification success in test data accuracy, precision, sensitivity and F1 Score.

Keywords

Thermal imaging, Breast lesion, Classification, Segmentation, Mask R-CNN, U-Net, Transfer learning

Derin Öğrenme Yardımıyla Aktif Termogramlar Üzerinden Meme Lezyonlarının Sınıflandırması

Abstract

Son yıllarda bilgisayar donanımları ile paralel olarak gelişim gösteren yapay zeka çalışmaları klinikte uzmanların erken teşhis ile olası metastazın önüne geçerek hasta sağ kalımını artırmaktadır. Literatürde klinikte kanser teşhisini gerçekleştiren çokça çalışma mevcuttur. Bu çalışmalarda, kanser sınıflandırmasının yapılması için makine öğrenmesi ve derin öğrenme uygulamaları sıklıkla uygulanmaktadır. Benzer şekilde çalışmada termal meme görüntüleri üzerinden derin öğrenme yöntemleri ile meme kanseri teşhisi ele alınmıştır. Çalışmada kullanılan görüntüler açık erişim olarak sunulan DMR-IR veri setinden alınmıştır. Veri setinden alınan görüntüler üzerinde bazı önişlemler yapılmış, ardından meme bölgelerinin bölütlenmesi için manuel ve otomatik olmak üzere iki farklı bölütleme metodu uygulanmıştır. Manuel bölütleme işleminde, VIA ile lokalizasyon bilgisi kaydedilen meme bölgelerinin maskesi oluşturup orijinal görüntüden çıkarılarak bölütleme gerçekleştirilmiştir. Otomatik bölütleme işleminde ise Mask R-CNN ve U-NET ile bölütleme yapılmıştır. Bu iki metot için bölütleme performans analizi yapılmış ve 0.9896 doğruluk, 0.9413 Dice ve 0.8900 Jaccard değerini gerçekleştiren Mask R-CNN ile sınıflandırma işlemleri çalışılmıştır. Manuel ve Mask-RCNN metodu ile bölütlenen görüntülerden oluşan termogramlar ile ön eğitimli yedi farklı (InceptionV3, MobileNet, MobileNetV2, ResNet50, VGG16, VGG19 ve Xception) mimari kullanılarak meme kanseri sınıflandırması gerçekleştirilmiştir. Sonuç olarak test verilerinde %100 sınıflandırma başarısını doğruluk, kesinlik, duyarlılık ve F1 Skoru ile MobileNet ve InceptionV3 mimarileri sağlamıştır.

Keywords

Termal görüntüleme, Meme lezyonu, Sınıflandırma, Bölütleme, Mask R-CNN, U-Net, Transfer öğrenme

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