Comparative Experimental Investigation and Application of Five Classic Pre-Trained Deep Convolutional Neural Networks via Transfer Learning for Diagnosis of Breast Cancer

• Autorzy: Nogay Hidir Selcuk , Akinci Tahir Cetin Akinci , Yilmaz Musa

Identyfikatory

DOI

10.12913/22998624/137964

• Języki publikacji: EN

Abstrakty

EN

In this study, for the diagnosis and classification of breast cancer, we used and applied five classical pre-trained deep convolutional neural network models (DCNN) which have proven successful many times in different fields (ResNet-18, AlexNet, GoogleNet and SuffleNet). To make pre-trained DCNN models suitable for the purpose of our study, we updated some layers according to the new situation by using the transfer learning technique. We did not change the weights of all layers used in these five pre-trained DCNN models. Instead, we just gave new weights to the new layers so that new layers adapt faster to emerging new DCNN models. With these five pre-trained DCNN models, we have realized a quadruple classification as "cancer", "normal", "actionable" and "benign", and a binary classification as "actionable + cancer" and "normal + benign". With these two separate classification and diagnosis studies, we have carried out comparative experimental examination and analysis of pre-trained DCNN models for breast cancer diagnosis. In the study, it was concluded that successful results can be achieved with pre-trained DCNN models without extra time-consuming procedures such as feature extraction, and DCNN can perform quite successfully in cancer diagnosis and image comment.

Słowa kluczowe

EN

breast cancer; classification; deep learning; DCNN; transfer learning; diagnosis

• Wydawca: Lublin University of Technology ; Polish Society of Ecological Engineering (PTIE), Branch of PTIE in Lublin
• Czasopismo: Advances in Science and Technology. Research Journal
• Rocznik: 2021
• Tom: Vol. 15, no 3
• Strony: 1--8
• Opis fizyczny: Bibliogr. 31 poz., fig., tab.

Twórcy

autor

Nogay Hidir Selcuk

• Department of Electrical and Energy, Kayseri University, Kayseri, Turkey

• https://orcid.org/0000-0001-9105-508X

autor

Akinci Tahir Cetin Akinci

• Department of Electrical Engineering, Istanbul Technical University, Istanbul, Turkey

• https://orcid.org/0000-0002-4657-6617

autor

Yilmaz Musa

• Department of Electrical and Electronics Eng., Batman University, Batman, Turkey

• https://orcid.org/0000-0002-2306-6008

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).