CAD of BCD from Thermal Mammogram Images Using Machine Learning

Abstract

Lump in the breast, discharge of blood from the nipple, and deformation of the nipple/breast and its texture are the symptoms of breast cancer. Though breast cancer is very common in women, men can also get breast cancer. In the early stages, BCD makes use of Thermal Mammograms Breast Images (TMBI). The cost of treatment can be severely reduced in the early stages of detection. Based on the techniques of segmentation, the Breast Cancer Detection (BCD) works. Moreover, by providing a balanced, reliable and appropriate second opinion, a tremendous role has been played by ML in medical practices due to enhanced Information and Communication Technology (ICT). For the purpose of making the whole detection process of Malignant Tumor (MT)/Benign Tumor (BT) very resourceful and time-efficient, there is now a possibility to form an automated and precise Computer-Aided Diagnosis System (CADs). Several Image Pattern Recognition Techniques were used to classify breast cancer using Thermal Mammograms Image Processing Techniques (TMIPT) in the present investigation. Presenting a new model to classify the BCD with the help of TMIPT, thermal imaging, and smart devices is the aim of this research article. Using well-designed experiments like Intensive Preoperative Radio Therapy (IPRT) and BCD, the implementation and valuation of a concrete application are carried out. This proposed method is for the automatic classification of TMBI of a similar standard so that the thermal camera of FLIR One Gen 3 One 3rd Generation (FLIR One Gen 3) that can be attached to the smart devices are capable of capturing BCD using Machine Learning (ML) algorithms. To imitate the behaviour of human Artificial Intelligence (AI), designing drug formulations, helping in clinical diagnosis and robotic surgery systems, finding medical statistical datasets, and decoding human diseases’ wireless network model as well as cancer are the reasons for the ML to empower the computer and robots. The outperformance of the ML models against all other classifiers and scoring impressively across heterogeneous performance metrics like 98.44% of Precision, 98.83% of Accuracy, and 100% of Recall are observed from the comparative analysis.