Intelligent Breast Cancer Detection in Thermography Images Using Extreme Learning Machine and Image Processing

Subject Areas : Intelligent signal and systems

Hamid Reza Khodadad ¹ , Homayoun Mahdavi-Nasab ²

1 - Department of Electrical Engineering- Najafabad Branch, Islamic Azad University, Najafabad, Iran
2 - Digital Processing and Machine Vision Research Center, Najafabad Branch, Islamic Azad University, Najafabad, Iran

Received: 2022-10-19 Accepted : 2023-02-14 Published : 2025-08-06

Keywords: automatic segmentation, breast thermography, feature extraction, local binary pattern, neural networks, extreme learning machine,

Abstract :

Since breast cancer is known as one of the main mortality reasons of women around the world, it would be most important to provide and develop early recognition methods for timely diagnosis, especially without using invasive imaging techniques and with fast response. Thermography with the contactless property and low cost, combined with a fast and reliable method to classify imaging results can be an ideal option to achieve this goal. This research aims at using breast thermography and a recent neural network method called extreme learning machine (ELM) as an intelligent and efficient classifier to recognize the cancer. We have also studied the development of this classifier as the kernel-based multilayer ELM. In the proposed method, at first, breast images are segmented using an automated segmentation to generate the region of interest (ROI) of the left and right breasts. Then, by extracting texture, color, and shape features, and presenting them separately or in combination with the neural network, the performance and efficiency of the method are evaluated. By experimenting with various models of local texture extraction, such as local binary pattern (LBP) and local ternary pattern (LTP), as well as using RGB and YC_bC_r color templates, the best results of this research in the database for mamma research with infrared image (DMR-IR) database belonged to the proposed combined LBP-Mix and the LTP texture features achieving more than 96% accuracy and 100% precision.

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