ArticleDiagnostics (Basel, Switzerland)2020
Optimization of Deep Learning Network Parameters Using Uniform Experimental Design for Breast Cancer Histopathological Image Classification.
Article in Diagnostics (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- MEAI: an artificial intelligence platform for predicting distant and lymph node metastases directly from primary breast cancer.Journal of cancer research and clinical oncology · 2023Article
- Analyzing Histological Images Using Hybrid Techniques for Early Detection of Multi-Class Breast Cancer Based on Fusion Features of CNN and Handcrafted.Diagnostics (Basel, Switzerland) · 2023Article
- Application of Deep Learning in Histopathology Images of Breast Cancer: A Review.Micromachines · 2022Review
- A novel adaptive momentum method for medical image classification using convolutional neural network.BMC medical imaging · 2022Article
- Deep Learning on Histopathology Images for Breast Cancer Classification: A Bibliometric Analysis.Healthcare (Basel, Switzerland) · 2021Article
- Histo-CADx: duo cascaded fusion stages for breast cancer diagnosis from histopathological images.PeerJ. Computer science · 2021Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Breast cancer, a common cancer type, is a major health concern in women. Recently, researchers used convolutional neural networks (CNNs) for medical image analysis and demonstrated classification performance for breast cancer diagnosis from within histopathological image datasets. However, the parameter settings of a CNN model are complicated, and using Breast Cancer Histopathological Database data for the classification is time-consuming. To overcome these problems, this study used a uniform experimental design (UED) and optimized the CNN parameters of breast cancer histopathological image classification. In UED, regression analysis was used to optimize the parameters. The experimental results indicated that the proposed method with UED parameter optimization provided 84.41% classification accuracy rate. In conclusion, the proposed method can improve the classification accuracy effectively, with results superior to those of other similar methods.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.