Evaluating the Effect of Noisy Thermal Images On the Detection of Early Breast Cancer Using Deep Learning

Breast cancer remains a leading cause of mortality among women globally. Some techniques have been developed to enhance early detection, among which thermal imaging has emerged as a promising modality capable of identifying potential signs of breast cancer in its early stages. In addition, Thermal images provide valuable pixel-level information by capturing temperature variations between healthy and cancerous tissues. However, the susceptibility of these thermal images to noise poses a challenge to diagnostic accuracy in the early stages. This research aims to assess the influence of various types of noise on the performance of recently developed deep-learning models designed for early breast cancer detection. In addition, a comprehensive analysis was conducted using a substantial dataset to assess the impact of noise on the models’ efficacy. It also encompasses different categories of noise, which are characterised by distinct mean and variance values ranging from 0.01 to 0.09. The findings reveal that introducing various types of noise, albeit within a small range of mean and variance values, adversely affects the performance of deep learning models. It shows that these filters play a pivotal role in enhancing classification accuracy.