Classification of Breast Thermal Images into Healthy/Cancer Group Using Pre-Trained Deep Learning Schemes
Document Type
Conference Proceeding
Publication Date
1-1-2022
Abstract
In the women's community, Breast Cancer (BC) is a severe disease. The World Health Organization reported in 2020 that 2.26 million deaths occur due to BC. BC is curable if detected early. Since thermal imaging is non-invasive and supports disease detection, it is commonly used in clinics. Compared to other methods, it keeps BC early and accurate. The proposed work aims to evaluate the performance of the Pretrained Deep-Learning Methods (PDLM) in detecting BC using the thermal images collected from the benchmark dataset. It includes the following stages: primary image processing, deep feature mining, handcrafted feature mining, feature optimization using Firefly-Algorithm (FA), classification and validation. Visual Lab thermal images were used in the study. The investigational outcome of this study authenticates that the VGG16, along with the DT, provides better detection accuracy (95.5%) compared to other classifiers used in this study. To justify the significance of the implemented technique, the proposed work not only improved accuracy, but also improved precision, sensitivity, specificity, and F1-Scores.
Publication Title
Procedia Computer Science
First Page Number
24
Last Page Number
34
DOI
10.1016/j.procs.2022.12.398
Recommended Citation
Kadry, Seifedine; Crespo, Rubén González; Herrera-Viedma, Enrique; Krishnamoorthy, Sujatha; and Rajinikanth, Venkatesan, "Classification of Breast Thermal Images into Healthy/Cancer Group Using Pre-Trained Deep Learning Schemes" (2022). Kean Publications. 670.
https://digitalcommons.kean.edu/keanpublications/670