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Deep learning capabilities for the categorization of microcalcification

Kumar Singh Koushlendra, Kumar Suraj, Antonakakis Marios, Moirogiorgou Konstantia, Deep Anirudh, Kashyap Kanchan L., Bajpai Manish K., Zervakis Michail

Απλή Εγγραφή


URIhttp://purl.tuc.gr/dl/dias/6A49AD7E-F9BE-4D3A-9472-BC695597F0F3-
Αναγνωριστικόhttps://doi.org/10.3390/ijerph19042159-
Αναγνωριστικόhttps://www.mdpi.com/1660-4601/19/4/2159-
Γλώσσαen-
Μέγεθος12 pagesen
ΤίτλοςDeep learning capabilities for the categorization of microcalcificationen
ΔημιουργόςKumar Singh Koushlendraen
ΔημιουργόςKumar Surajen
ΔημιουργόςAntonakakis Mariosen
ΔημιουργόςΑντωνακακης Μαριοςel
ΔημιουργόςMoirogiorgou Konstantiaen
ΔημιουργόςΜοιρογιωργου Κωνσταντιαel
ΔημιουργόςDeep Anirudhen
ΔημιουργόςKashyap Kanchan L.en
ΔημιουργόςBajpai Manish K.en
ΔημιουργόςZervakis Michailen
ΔημιουργόςΖερβακης Μιχαηλel
ΕκδότηςMDPIen
ΠερίληψηBreast 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
ΤύποςPeer-Reviewed Journal Publicationen
ΤύποςΔημοσίευση σε Περιοδικό με Κριτέςel
Άδεια Χρήσηςhttp://creativecommons.org/licenses/by/4.0/en
Ημερομηνία2023-09-14-
Ημερομηνία Δημοσίευσης2022-
Θεματική ΚατηγορίαCanceren
Θεματική ΚατηγορίαMicrocalcificationen
Θεματική ΚατηγορίαConvolution neural networken
Θεματική ΚατηγορίαBiomedical imagingen
Θεματική ΚατηγορίαMammogramsen
Βιβλιογραφική ΑναφοράK. 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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