A Review on Recent Progress in Thermal Imaging and Deep Learning Approaches for Breast Cancer Detection

Abstract

Developing a breast cancer screening method is very important to facilitate early breast cancer detection and treatment. Building a screening method using medical imaging modality that does not cause body tissue damage (non-invasive) and does not involve physical touch is challenging. Thermography, a non-invasive and non-contact cancer screening method, can detect tumors at an early stage even under precancerous conditions by observing temperature distribution in both breasts. The thermograms obtained on thermography can be interpreted using deep learning models such as convolutional neural networks (CNNs). CNNs can automatically classify breast thermograms into categories such as normal and abnormal. Despite their demostrated utility, CNNs have not been widely used in breast thermogram classification. In this study, we aimed to summarize the current work and progress in breast cancer detection based on thermography and CNNs. We first discuss of breast thermography potential in early breast cancer detection, providing an overview of the availability of breast thermal datasets together with publicly accessible. We also discuss characteristics of breast thermograms and the differences between healthy and cancerous thermographic patterns. Breast thermogram classification using a CNN model is described step by step including a simulation example illustrating feature learning. We cover most research related to the implementation of deep neural networks for breast thermogram classification and propose future research directions for developing representative datasets, feeding the segmented image, assigning a good kernel, and building a lightweight CNN model to improve CNN performance. INDEX TERMS breast cancer; convolutional neural network; deep learning; early detection; thermogram

Kızılötesi Termal Görüntülemenin Yapay Zeka Tabanlı Biyomedikal Uygulamaları

“…Yaşam tarzı faktörlerinin değişmesi, endüstrilerin hızlı büyümesi nedeniyle, kentleşme meme kanseri insidansında kademeli bir artışa neden olmuştur [37]. Meme kanserinin erken teşhisi, uygun tedaviyi sağlayarak kadınların hayatta kalma oranını artırmaktadır [37][38][39]. Termal görüntüleme ile meme asimetrisinin değerlendirilmesi ve uygun yapay zekâ tekniklerinin kullanımı erken teşhiste önemli bir rol oynamaktadır.…”

“…Böylelikle sınıflandırma sonucu çıktı olarak gözlenebilir. Şekil 3, göğüs termogramlarından hasta-sağlıklı ayrımı yapan ESA'ların genel mimarisini göstermektedir [39].…”

A Systematic Review of Breast Cancer Detection Using Thermography and Neural Networks

“…Surveys have been conducted in relation to application of automated systems in cancer detection such as computer aided diagnostic systems [66] with precursor attempts appearing in [68], thermography, infrared thermography and electrical impedance tomography [70], highly diversified early attempts at automated systems [71], standards and protocols to Infrared imaging technology for breast cancer detection [72], CNN based thermal imaging for breast cancer detection [79] and various screening tools to detect breast cancer [73]. There are no emphasis on the thermal camera used, image acquisition procedures for mobile phones and public databases.…”

Infrared Thermal Images Classification for Pressure Injury Prevention Incorporating the Convolutional Neural Networks

“…The most discriminative features based on wavelet transformation in breast tumor images were obtained. Roslidar et al [12] have introduced the application of CNN in the breast cancer based on thermal images, providing the summary of the current work on thermal images and an overview of the availability of breast thermal images. Juan Zuluaga-Gomez et al [13] proposed a CNN hyperparameters fine-tuning optimization algorithm, identifying the breast cancer through infrared thermal imaging and the classification accuracy is 92%.…”