Deep Learning Capabilities for the Categorization of Microcalcification

Koushlendra Kumar Singh, Suraj Kumar, Marios Antonakakis, Konstantina Moirogiorgou, Anirudh Deep, Kanchan Lata Kashyap, Manish Kumar Bajpai and Michalis Zervakis
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Koushlendra Kumar Singh: Machine Vision and Intelligence Lab, Department of Computer Science and Engineering, National Institute of Technology, Jamshedpur 831014, India
Suraj Kumar: Machine Vision and Intelligence Lab, Department of Computer Science and Engineering, National Institute of Technology, Jamshedpur 831014, India
Marios Antonakakis: Digital Image and Signal Processing Laboratory, School of Electrical and Computer Engineering, Technical University of Crete, 73100 Crete, Greece
Konstantina Moirogiorgou: Digital Image and Signal Processing Laboratory, School of Electrical and Computer Engineering, Technical University of Crete, 73100 Crete, Greece
Anirudh Deep: Machine Vision and Intelligence Lab, Department of Computer Science and Engineering, National Institute of Technology, Jamshedpur 831014, India
Kanchan Lata Kashyap: Department of Computer Science and Engineering, Vellore Institute of Technology University, Bhopal 466114, India
Manish Kumar Bajpai: Computer Science and Engineering Discipline, PDPM Indian Institute of Information Technology Design Manufacturing, Jabalpur 482005, India
Michalis Zervakis: Digital Image and Signal Processing Laboratory, School of Electrical and Computer Engineering, Technical University of Crete, 73100 Crete, Greece

IJERPH, 2022, vol. 19, issue 4, 1-12

Abstract: 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.

Keywords: cancer; microcalcification; convolution neural network; biomedical imaging; mammograms (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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