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Technical University of Crete
Technical University of Crete
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| URI | http://purl.tuc.gr/dl/dias/6A49AD7E-F9BE-4D3A-9472-BC695597F0F3 | - |
| Identifier | https://doi.org/10.3390/ijerph19042159 | - |
| Identifier | https://www.mdpi.com/1660-4601/19/4/2159 | - |
| Language | en | - |
| Extent | 12 pages | en |
| Title | Deep learning capabilities for the categorization of microcalcification | en |
| Creator | Kumar Singh Koushlendra | en |
| Creator | Kumar Suraj | en |
| Creator | Antonakakis Marios | en |
| Creator | Αντωνακακης Μαριος | el |
| Creator | Moirogiorgou Konstantia | en |
| Creator | Μοιρογιωργου Κωνσταντια | el |
| Creator | Deep Anirudh | en |
| Creator | Kashyap Kanchan L. | en |
| Creator | Bajpai Manish K. | en |
| Creator | Zervakis Michail | en |
| Creator | Ζερβακης Μιχαηλ | el |
| Publisher | MDPI | en |
| Content Summary | 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. | en |
| Type of Item | Peer-Reviewed Journal Publication | en |
| Type of Item | Δημοσίευση σε Περιοδικό με Κριτές | el |
| License | http://creativecommons.org/licenses/by/4.0/ | en |
| Date of Item | 2023-09-14 | - |
| Date of Publication | 2022 | - |
| Subject | Cancer | en |
| Subject | Microcalcification | en |
| Subject | Convolution neural network | en |
| Subject | Biomedical imaging | en |
| Subject | Mammograms | en |
| Bibliographic Citation | K. Kumar-Singh, S. Kumar, M. Antonakakis, K. Moirogiorgou, A. Deep, K. L. Kashyap, M. K. Bajpai, and M. Zervakis, “Deep learning capabilities for the categorization of microcalcification,” Int. J. Environ. Res. Public Health, vol. 19, no. 4, Feb. 2022, doi: 10.3390/ijerph19042159. | en |
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