Early detection of thermal image based T1 breast cancer using enhanced multiwavelet denoised convolution neural network with region based analysis

Abstract

Medical Thermography image is used for the detection of breast cancer at an earlier stage. Thermography image shows the temperature change in the body due to cells. The metabolic rate of cancer cells is high compared to normal cells. A high metabolic rate increases the blood flow in cancer cells. High blood leads to changes in body temperature. The change in body temperature is used for cancer cell detection at an earlier stage. However, T1-stage cancer cells are smaller and have small temperature differences undetected with thermography. In this paper, a T1-stage cancer cell is heated by an external source; then, thermal images are acquired for earlier detection of small-size cancer cells. External heat source amplifies T1 stage cancer cell temperature. Amplified cancer cell images are analyzed using the proposed Multiwavelet-Deep Denoised Convolutional Neural Network (MWTDnCNN) algorithm for T1 cancer cell detection. Amplified T1 stage cancer cell has higher thermal conductivity (k) and heat capacity (Cp), which helps to detect T1 cancer cell tissue due to the enhanced pixel feature. The proposed MWTDnCNN algorithm has a T1-stage cancer cell detection accuracy of about 98% compared with traditional algorithms. The proposed MWTDnCNN algorithm detects T1-stage cancer of size 1.29 mm.

Keywords

• Multiwavelet denoised convolution neural network
• thermal conductivity
• heat capacity
• region properties