Breast cancer detection from thermal images using a Grunwald-Letnikov-aided Dragonfly algorithm-based deep feature selection method. (February 2022) - Chatterjee, Somnath; Biswas, Shreya; Majee, Arindam; Sen, Shibaprasad; Oliva, Diego; Sarkar, Ram
Abstract: Breast cancer is one of the deadliest diseases in women and its incidence is growing at an alarming rate. However, early detection of this disease can be life-saving. The rapid development of deep learning techniques has generated a great deal of interest in the medical imaging field. Researchers around the world are working on developing breast cancer detection methods using medical imaging. In the present work, we have proposed a two-stage model for breast cancer detection using thermographic images. Firstly, features are extracted from images using a deep learning model, called VGG16. To select the optimal subset of features, we use a meta-heuristic algorithm called the Dragonfly Algorithm (DA) in the second step. To improve the performance of the DA, a memory-based version of DA is proposed using the Grunwald-Letnikov (GL) method. The proposed two-stage framework has been evaluated on a publicly available standard dataset called DMR-IR. The proposed model efficiently filters out non-essential features and had 100% diagnostic accuracy on the standard dataset, with 82% fewer features compared to the VGG16 model. Highlights: We combined a deep neural network with meta-heuristic optimization to detect breast cancer. We used transfer learning to avoid over-fitting of the CNN model and a small dataset. We propose a modified version of Dragonfly Algorithm to reduce the feature dimension. We achieved impressive results on the publicly available DMR-IR breast cancer dataset.