计算机科学
人工智能
算法
乳腺癌
深度学习
特征选择
特征(语言学)
启发式
机器学习
模式识别(心理学)
癌症
医学
语言学
内科学
哲学
作者
Somnath Chatterjee,Shreya Biswas,Arindam Majee,Shibaprasad Sen,Diego Oliva,Ram Sarkar
标识
DOI:10.1016/j.compbiomed.2021.105027
摘要
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.
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