Ιδρυματικό Αποθετήριο
Πολυτεχνείο Κρήτης
Πολυτεχνείο Κρήτης
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| URI | http://purl.tuc.gr/dl/dias/6A49AD7E-F9BE-4D3A-9472-BC695597F0F3 | - |
| Αναγνωριστικό | https://doi.org/10.3390/ijerph19042159 | - |
| Αναγνωριστικό | https://www.mdpi.com/1660-4601/19/4/2159 | - |
| Γλώσσα | en | - |
| Μέγεθος | 12 pages | en |
| Τίτλος | Deep learning capabilities for the categorization of microcalcification | en |
| Δημιουργός | Kumar Singh Koushlendra | en |
| Δημιουργός | Kumar Suraj | en |
| Δημιουργός | Antonakakis Marios | en |
| Δημιουργός | Αντωνακακης Μαριος | el |
| Δημιουργός | Moirogiorgou Konstantia | en |
| Δημιουργός | Μοιρογιωργου Κωνσταντια | el |
| Δημιουργός | Deep Anirudh | en |
| Δημιουργός | Kashyap Kanchan L. | en |
| Δημιουργός | Bajpai Manish K. | en |
| Δημιουργός | Zervakis Michail | en |
| Δημιουργός | Ζερβακης Μιχαηλ | el |
| Εκδότης | MDPI | en |
| Περίληψη | 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 |
| Τύπος | Peer-Reviewed Journal Publication | en |
| Τύπος | Δημοσίευση σε Περιοδικό με Κριτές | el |
| Άδεια Χρήσης | http://creativecommons.org/licenses/by/4.0/ | en |
| Ημερομηνία | 2023-09-14 | - |
| Ημερομηνία Δημοσίευσης | 2022 | - |
| Θεματική Κατηγορία | Cancer | en |
| Θεματική Κατηγορία | Microcalcification | en |
| Θεματική Κατηγορία | Convolution neural network | en |
| Θεματική Κατηγορία | Biomedical imaging | en |
| Θεματική Κατηγορία | Mammograms | en |
| Βιβλιογραφική Αναφορά | 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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