Thermography as an Economical Alternative Modality to Mammography for Early Detection of Breast Cancer.

Saad Alqithami,Asim Ali Khan,Ajat Shatru Arora

doi:10.1155/2021/5543101

Abstract

Breast cancer has become a menacing form of cancer among women accounting for 11.6% of total deaths of 9.6 million due to all types of cancer every year all over the world. Early detection increases chances of survival and reduces the cost of treatment as well. Screening modalities such as mammography or thermography are used to detect cancer early; thus, several lives can be saved with timely treatment. But, there are interpretational failures on the part of the radiologists to read the mammograms or thermograms and also there are interobservational and intraobservational differences between them. So, the degree of variations among the different radiologists in the interpretation of results is very high resulting in false positives and false negatives. The double reading can reduce the human errors involved in the interpretation of mammograms. But, the limited number of medical professionals in developing or underdeveloped countries puts a limitation on this remedial way. So, a computer-aided system (CAD) is proposed to detect the benign cases from the abnormal cases that can result in automatic detection of breast cancer or can provide a double reading in the case of nonavailability of the trained medical professionals in developing economies. The generally accepted screening modality is mammography for the early detection of cancer. But thermography has been tried for early detection of breast cancer in recent times. The high metabolic activity of the cancer cells results in an early change in the temperature profile of the region. This shows asymmetry between normal and cancerous breast which can be detected using different techniques. Thus, this work is focussed on the use of thermography in the early detection of breast cancer. An experimental study is conducted to find the results of classification accuracy to compare the efficacy of thermography and mammography in classifying the normal from abnormal ones and further abnormal ones into benign and malignant cases. Thermography is found to have classification accuracy almost at par with mammography for classifying the cancerous breasts from healthy ones with classification accuracies of thermography and mammography being 96.57% and 98.11%, respectively. Thermography is found to have much better accuracy in identifying benign cases from the malignant ones with the classification accuracy of 92.70% as compared to 82.05% with mammography. This will result in the early detection of cancer. The advantage of being portable and inexpensive makes thermography an attractive modality to be used in economically backward rural areas where mammography is not practically possible.

Highlights

Cancer is an uncontrolled growth of cells in any part of the body. e cells which grow in an uncontrolled manner forming abnormal cells are called cancerous cells
As the false positives in this classification can result in an unnecessary biopsy, the false negative will miss out the malignant cases. is is one of the fallouts of mammographic imaging that the classification accuracy is quite less in benign/malignant classification
Mammograms are classified into normal and cancerous ones using the SVM classifiers with an average accuracy of 98.11% as compared to the average accuracy of 96.57% achieved with thermography. e performances of the two modalities are almost the same

Summary

Introduction

Cancer is an uncontrolled growth of cells in any part of the body. e cells which grow in an uncontrolled manner forming abnormal cells are called cancerous cells. E significant research funding results in the early detection and treatment of breast cancer. 2. Literature Review ere are a number of research works on breast cancer detection using thermography in recent times. An automatic method is proposed employing many image-processing techniques such as thresholding, clustering, edge detection, and refinement, for the segmentation of the thermograms [5]. Horizontal edge detection followed by Otsu’s thresholding and morphological operation is used in this paper to separate the left and right breasts with good results. Ere are a number of research works on breast cancer detection using mammography available in the literature. E most commonly used texture features in mammographic images are described in [12] by Haralick et al A number of methods of classifications, both unsupervised and supervised, are used to classify the mammograms. For identifying a minute difference in the temperature pattern of both breasts, asymmetry analysis of both breasts is done using the

Background removed

Discussion

Findings

Methodology