Diagnosis of Breast Cancer by Integrating Machine Learning and Machine Vision Techniques in Thermography Images

EasyChair Preprint 5331

Abstract

Breast cancer has increased among women in recent years and is one of the leading causes of death in women. Studies show that thermography is a faster, cheaper, passive, risk-free, radiation-free and pain-free method than other diagnostic methods. New methods of image processing, vision and machine learning have led to successful investigations into the invention of breast cancer detection systems by thermometric images. In the present study, a proper method of diagnosing abnormality through thermography images of the obverse view is presented. By this segregation method, the breast area and every other area targeted by the physician that is vital for breast cancer diagnosis are color-divided in the thermographs. Warmer regions known as vital centers are extracted by the FCM algorithm and the fractal dimension of these regions is calculated using three different methods. In this study, for the first time, we used fractal analysis to analyze the symmetrical heat distribution in two breast tissues. The results suggesting that fractal analysis may potentially improve the reliability of thermography in breast tumor detection. Fractal analysis also plays an important role in tracking the symmetrical heat distribution in two breast tissues to detect abnormalities.

Keyphrases: Fuzzy C-Means, fractal dimension, segregation area, symmetrical temperature distribution analysis

@booklet{EasyChair:5331,
  author    = {Behzad Lak and Parastoo Najafi},
  title     = {Diagnosis of Breast Cancer by Integrating Machine Learning and Machine Vision Techniques in Thermography Images},
  howpublished = {EasyChair Preprint 5331},
  year      = {EasyChair, 2021}}