2023
Fauzan, D. F.; Fauzi, R.; Pratiwi, O. N.; Machado, J. M. Ferreira
Breast Cancer Detection on Histopathology Images Using Pre-trained Computer Vision Models Proceedings Article
Em: Institute of Electrical and Electronics Engineers Inc., 2023, ISBN: 9798350303414, (cited By 0; Conference of 5th International Conference on Advancement in Data Science, E-Learning and Information Systems, ICADEIS 2023 ; Conference Date: 2 August 2023 Through 3 August 2023; Conference Code:193233).
Resumo | Links | BibTeX | Etiquetas: Breast Cancer; Breast cancer detection; Cancer detection; Deep learning; Learning models; Learning techniques; Specific tasks; Transfer learning; Vision model; World Health Organization, Computer vision, Deep learning; Diseases; Learning systems; Medical imaging; Transfer learning
@inproceedings{Fauzan2023,
title = {Breast Cancer Detection on Histopathology Images Using Pre-trained Computer Vision Models},
author = {D. F. Fauzan and R. Fauzi and O. N. Pratiwi and J. M. Ferreira Machado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85175636654&doi=10.1109%2fICADEIS58666.2023.10270900&partnerID=40&md5=0526f55af1d7651d110203f5f4f0b012},
doi = {10.1109/ICADEIS58666.2023.10270900},
isbn = {9798350303414},
year = {2023},
date = {2023-01-01},
journal = {ICADEIS 2023 - International Conference on Advancement in Data Science, E-Learning and Information Systems: Data, Intelligent Systems, and the Applications for Human Life, Proceeding},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Breast cancer is the most common type of cancer worldwide. According to the World Health Organization (WHO), there were 7.8 million women alive in 2020 who had been diagnosed with breast cancer, and it has claimed more women's lives than any other kind of cancer. With the recent rise of artificial intelligence, breast cancer detection using deep learning techniques is getting more popular. However, creating a deep learning model for a specific task from scratch costs a lot of time and money. Transfer learning is a well-known method that can make deep learning developments more efficient by leveraging pre-trained models. Using the BreakHis dataset, this paper will compare three cutting-edge pre-trained computer vision models: DenseNet, RegNet, and BiT, in predicting malignant or benign tumor tissue from breast histopathology images to determine which model is better for that specific task. Although the DenseNet model achieves the highest score with 93.7% Area Under the ROC Curve (AUC) and 97.4% Average Precision Score (APS), the BiT model is more suitable for deployment in a real-world setting since it can predict more malignant cases correctly than the other two models with a sensitivity score of 90.79%. © 2023 IEEE.},
note = {cited By 0; Conference of 5th International Conference on Advancement in Data Science, E-Learning and Information Systems, ICADEIS 2023 ; Conference Date: 2 August 2023 Through 3 August 2023; Conference Code:193233},
keywords = {Breast Cancer; Breast cancer detection; Cancer detection; Deep learning; Learning models; Learning techniques; Specific tasks; Transfer learning; Vision model; World Health Organization, Computer vision, Deep learning; Diseases; Learning systems; Medical imaging; Transfer learning},
pubstate = {published},
tppubtype = {inproceedings}
}
Breast cancer is the most common type of cancer worldwide. According to the World Health Organization (WHO), there were 7.8 million women alive in 2020 who had been diagnosed with breast cancer, and it has claimed more women's lives than any other kind of cancer. With the recent rise of artificial intelligence, breast cancer detection using deep learning techniques is getting more popular. However, creating a deep learning model for a specific task from scratch costs a lot of time and money. Transfer learning is a well-known method that can make deep learning developments more efficient by leveraging pre-trained models. Using the BreakHis dataset, this paper will compare three cutting-edge pre-trained computer vision models: DenseNet, RegNet, and BiT, in predicting malignant or benign tumor tissue from breast histopathology images to determine which model is better for that specific task. Although the DenseNet model achieves the highest score with 93.7% Area Under the ROC Curve (AUC) and 97.4% Average Precision Score (APS), the BiT model is more suitable for deployment in a real-world setting since it can predict more malignant cases correctly than the other two models with a sensitivity score of 90.79%. © 2023 IEEE.