| Autor: |
Agbley BLY; School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China., Li J; School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China., Hossin MA; School of Innovation and Entrepreneurship, Chengdu University, Chengdu 610106, China., Nneji GU; School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China., Jackson J; School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China., Monday HN; School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China., James EC; School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China. |
| Jazyk: |
angličtina |
| Zdroj: |
Diagnostics (Basel, Switzerland) [Diagnostics (Basel)] 2022 Jul 09; Vol. 12 (7). Date of Electronic Publication: 2022 Jul 09. |
| DOI: |
10.3390/diagnostics12071669 |
| Abstrakt: |
Invasive carcinoma of no special type (IC-NST) is known to be one of the most prevalent kinds of breast cancer, hence the growing research interest in studying automated systems that can detect the presence of breast tumors and appropriately classify them into subtypes. Machine learning (ML) and, more specifically, deep learning (DL) techniques have been used to approach this problem. However, such techniques usually require massive amounts of data to obtain competitive results. This requirement makes their application in specific areas such as health problematic as privacy concerns regarding the release of patients' data publicly result in a limited number of publicly available datasets for the research community. This paper proposes an approach that leverages federated learning (FL) to securely train mathematical models over multiple clients with local IC-NST images partitioned from the breast histopathology image (BHI) dataset to obtain a global model. First, we used residual neural networks for automatic feature extraction. Then, we proposed a second network consisting of Gabor kernels to extract another set of features from the IC-NST dataset. After that, we performed a late fusion of the two sets of features and passed the output through a custom classifier. Experiments were conducted for the federated learning (FL) and centralized learning (CL) scenarios, and the results were compared. Competitive results were obtained, indicating the positive prospects of adopting FL for IC-NST detection. Additionally, fusing the Gabor features with the residual neural network features resulted in the best performance in terms of accuracy, F1 score, and area under the receiver operation curve (AUC-ROC). The models show good generalization by performing well on another domain dataset, the breast cancer histopathological (BreakHis) image dataset. Our method also outperformed other methods from the literature. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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