Breast Cancer Detection in Thermal Infrared Images Using Representation Learning and Texture Analysis Methods

Abstract

Nowadays, breast cancer is one of the most common cancers diagnosed in women. Mammography is the standard screening imaging technique for the early detection of breast cancer. However, thermal infrared images (thermographies) can be used to reveal lesions in dense breasts. In these images, the temperature of the regions that contain tumors is warmer than the normal tissue. To detect that difference in temperature between normal and cancerous regions, a dynamic thermography procedure uses thermal infrared cameras to generate infrared images at fixed time steps, obtaining a sequence of infrared images. In this paper, we propose a novel method to model the changes on temperatures in normal and abnormal breasts using a representation learning technique called learning-to-rank and texture analysis methods. The proposed method generates a compact representation for the infrared images of each sequence, which is then exploited to differentiate between normal and cancerous cases. Our method produced competitive (AUC = 0.989) results when compared to other studies in the literature.

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

“…A study [120] applied a multilayer perceptron DNN model to classify breast thermogram in four classes. Although its accuracy was at 95%, feature extraction was performed con-ventionally.…”

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

“…In [42], researcher modeled the changes on temperatures in normal and abnormal breasts using a representation learning technique called learning-to-rank and texture analysis methods with multilayer perceptron (MLP) classifier. Dynamic thermal image database DMR-IR was used in the study with four experiments to evaluate the performance of the network.…”

A Computer Vision Line-Tracking Algorithm for Automatic UAV Photovoltaic Plants Monitoring Applications

“…In the computer vision literature, several possibilities to extract and analyze features exist. In particular, feature descriptors algorithms like Scale Invariant Feature Transform (SIFT) [30], Speeded Up Robust Features (SURF) [31], Histogram of Oriented Gradients (HOG) [32], and Local Binary Patterns (LBP) have been used in a wide range of applications, including face recognition [33], speech resampling [34], and cancer detection [35]. However, for the application object of this paper, since the shape to be searched in the image is known a priori and very simple, i.e., a line, the extraction process can be divided into an edge detection and a simple line detection algorithm.…”