Deep Learning Capabilities for the Categorization of Microcalcification

International Journal of Environmental Research and Public Health

Manish Kumar Bajpai¹  Kanchan Lata Kashyap²  Marios Antonakakis³  Michalis Zervakis³  Konstantina Moirogiorgou³  Koushlendra Kumar Singh⁴  Anirudh Deep⁴  Suraj Kumar⁴ 

[1] Computer Science and Engineering Discipline, PDPM Indian Institute of Information Technology Design Manufacturing, Jabalpur 482005, India;Department of Computer Science and Engineering, Vellore Institute of Technology University, Bhopal 466114, India;Digital Image and Signal Processing Laboratory, School of Electrical and Computer Engineering, Technical University of Crete, 73100 Crete, Greece;Machine Vision and Intelligence Lab, Department of Computer Science and Engineering, National Institute of Technology, Jamshedpur 831014, India;

关键词: cancer;    microcalcification;    convolution neural network;    biomedical imaging;    mammograms;   

DOI  :  10.3390/ijerph19042159

来源: DOAJ

【 摘 要 】

Breast cancer is the most common cancer in women worldwide. It is the most frequently diagnosed cancer among women in 140 countries out of 184 reporting countries. Lesions of breast cancer are abnormal areas in the breast tissues. Various types of breast cancer lesions include (1) microcalcifications, (2) masses, (3) architectural distortion, and (4) bilateral asymmetry. Microcalcification can be classified as benign, malignant, and benign without a callback. In the present manuscript, we propose an automatic pipeline for the detection of various categories of microcalcification. We performed deep learning using convolution neural networks (CNNs) for the automatic detection and classification of all three categories of microcalcification. CNN was applied using four different optimizers (ADAM, ADAGrad, ADADelta, and RMSProp). The input images of a size of 299 × 299 × 3, with fully connected RELU and SoftMax output activation functions, were utilized in this study. The feature map was obtained using the pretrained InceptionResNetV2 model. The performance evaluation of our classification scheme was tested on a curated breast imaging subset of the DDSM mammogram dataset (CBIS–DDSM), and the results were expressed in terms of sensitivity, specificity, accuracy, and area under the curve (AUC). Our proposed classification scheme outperforms the ability of previously used deep learning approaches and classical machine learning schemes.

【 授权许可】

Unknown