TY  - JOUR
T1  - The Advantages of Employing Transfer Learning in the Classification of Breast Cancer Histopathological Images
AU  - Moldovanu, Simona
AU  - Raducan, Elena
AU  - Miron, Mihaela
AU  - Sîrbu, Carmen
PY  - 2025
DA  - July
Y2  - 2025
DO  - 10.55549/epstem.1731520
JF  - The Eurasia Proceedings of Science Technology Engineering and Mathematics
JO  - EPSTEM
PB  - ISRES Publishing
WT  - DergiPark
SN  - 2602-3199
SP  - 140
EP  - 147
VL  - 33
LA  - en
AB  - Digital pathology represents a significant advancement in contemporary medicine, offering enhanced diagnostic capabilities and improved patient outcomes. Pathological examinations, which need particular steps in the diagnostic process, are standard in medical protocols and the law. Today, a new challenge is to use cutting-edge algorithms, like Convolutional Neural Networks (CNN), to classify histological images into different groups. So, the Invasive Ductal Carcinoma (IDC) dataset was used to use some well-known CNNs, such as VGG16, DenseNet169, and EfficientNetV2B3 pre-trained networks, as well as two new custombuilt CNNs with four (CNN1) and five (CNN2) layers. The results show that for a 70% training to 30% testing ratio, CNN1 (0.895), CNN2 (0.882), VGG16 (0.983), DenseNet169 (0.971), and EfficientNetV2B3 (0.979) all got the best results on the test set. The results obtained with pre-trained CNNs are superior to proposed custombuilt CNNs. This outcome denotes the main advantage of leveraging pre-trained CNNs in classifying breast cancer histopathological images.
KW  - Convolutional neural networks
KW  - VGG16
KW  - DenseNet169
KW  - EfficientNetV2B3
CR  - Moldovanu, S., Raducan, E., Miron, M., & Sirbu, C. (2025). The advantages of employing transfer learning in the classification of breast cancer histopathological images. The Eurasia Proceedings of Science, Technology, Engineering and Mathematics (EPSTEM), 33, 140-147.
UR  - https://doi.org/10.55549/epstem.1731520
L1  - https://dergipark.org.tr/en/download/article-file/5009311
ER  -