Exploring deep learning networks for tumour segmentation in infrared images. Issue 3 (2nd July 2020)

• Record Type: Journal Article
• Authors: Kakileti, Siva Teja; Dalmia, Aman; Manjunath, Geetha
• Is Part Of: Quantitative infrared thermography. Volume 17:Issue 3(2020)
• Page Start: 153
• Page End: 168
• Publication Date: 2020-07-02
• Subjects: Hotspot segmentation -- tumour detection -- convolution neural networks -- breast cancer; Infrared technology -- Periodicals; Thermography -- Periodicals; Infrared technology; Thermography; Periodicals; 621.362
• DOI: 10.1080/17686733.2019.1619355
• Languages: English
• ISSNs: 1768-6733

Abstract

ABSTRACT: Infrared imaging has been used for detecting early-stage breast abnormalities. However, manual interpretation of these images is subjective and error prone. Automated analysis of thermal images using artificial intelligence increases the accuracy of detecting breast cancer and enables use in breast cancer screening programmes. A crucial step in automated analysis is tumour segmentation, which is the focus of this paper. Prior algorithms used for tumour segmentation involve clustering, thresholding and active contour techniques that rely on low-level image features generated from manual feature engineering. The recent advances in deep learning, especially convolutional neural networks (CNNs), are slowly making them the de-facto method for automated image analysis. In this paper, we explore various CNN architectures for semantic segmentation starting from naive patch-based classifiers to more sophisticated ones such as encoder-decoder architecture for detecting the hotspots in the thermal image. We also show that encoder-decoder architectures perform better when compared to patch-based classifiers in terms of accuracy, Dice index, Jaccard index and inference time even with a smaller dataset.