Izvorni znanstveni članak
https://doi.org/10.1080/00051144.2024.2413219
Early detection of thermal image based T1 breast cancer using enhanced multiwavelet denoised convolution neural network with region based analysis
P. Geetha
; Department of Electronics and Instrumentation Engineering, SRM Institute of Science and Technology, Chennai, India
*
S. UmaMaheswari
; Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Chennai, India
* Dopisni autor.
Sažetak
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.
Ključne riječi
Multiwavelet denoised convolution neural network; thermal conductivity; heat capacity; region properties
Hrčak ID:
326444
URI
Datum izdavanja:
10.10.2024.
Posjeta: 0 *