Chatterjee Somnath, Biswas Shreya, Majee Arindam, Sen Shibaprasad, Oliva Diego, Sarkar Ram
Future Institute of Engineering and Management, Kolkata, West Bengal, India.
Jadavpur University, Kolkata, West Bengal, India.
Comput Biol Med. 2022 Feb;141:105027. doi: 10.1016/j.compbiomed.2021.105027. Epub 2021 Nov 14.
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.
乳腺癌是女性最致命的疾病之一，其发病率正以惊人的速度增长。然而，早期发现这种疾病可以挽救生命。深度学习技术的快速发展在医学成像领域引起了极大的兴趣。世界各地的研究人员正在致力于开发使用医学成像的乳腺癌检测方法。在当前的工作中，我们提出了一种使用热成像图像进行乳腺癌检测的两阶段模型。首先，使用一个名为VGG16的深度学习模型从图像中提取特征。为了选择最优的特征子集，我们在第二步中使用了一种名为蜻蜓算法（DA）的元启发式算法。为了提高DA的性能，使用格伦沃尔德 - 莱尼科夫（GL）方法提出了一种基于记忆的DA版本。所提出的两阶段框架已在一个名为DMR - IR的公开可用标准数据集上进行了评估。所提出的模型有效地过滤掉了非必要特征，在标准数据集上具有100%的诊断准确率，与VGG16模型相比，特征数量减少了82%。
