ArticleScientific reports2022
Performance analysis of seven Convolutional Neural Networks (CNNs) with transfer learning for Invasive Ductal Carcinoma (IDC) grading in breast histopathological images.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
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- PatchSight-ImmuneMap-LifeSpan as a unified AI framework for breast cancer diagnosis, immune profiling and prognostic prediction.Discover oncology · 2026Article
- Hybrid deep learning framework MedFusionNet assists multilabel biomedical risk stratification from imaging and tabular data.Communications medicine · 2026Article
- Artificial Intelligence as an Assistive Tool in Breast Cancer Screening and Diagnosis in the Digital Era.International journal of breast cancer · 2026Article
- The Role of ChatGPT in Dermatology Diagnostics.Diagnostics (Basel, Switzerland) · 2025Review
- Applicability of the regression approach for histological multi-class grading in clear cell renal cell carcinoma.Regenerative therapy · 2025Article
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- DIEANet: an attention model for histopathological image grading of lung adenocarcinoma based on dimensional information embedding.Scientific reports · 2024Article
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- Evaluating the effectiveness of stain normalization techniques in automated grading of invasive ductal carcinoma histopathological images.Scientific reports · 2023Article
- Non-annotated renal histopathological image analysis with deep ensemble learning.Quantitative imaging in medicine and surgery · 2023Article
- Lymphocyte detection for cancer analysis using a novel fusion block based channel boosted CNN.Scientific reports · 2023Article
- Histopathological Gastric Cancer Detection on GasHisSDB Dataset Using Deep Ensemble Learning.Diagnostics (Basel, Switzerland) · 2023Article
- Breast lesion identification using feature fusion and multiresolution dual-tree complex wavelet transform.Digital healthArticle
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Authors and funding
8 authors.
Funding
Abstract
Computer-aided Invasive Ductal Carcinoma (IDC) grading classification systems based on deep learning have shown that deep learning may achieve reliable accuracy in IDC grade classification using histopathology images. However, there is a dearth of comprehensive performance comparisons of Convolutional Neural Network (CNN) designs on IDC in the literature. As such, we would like to conduct a comparison analysis of the performance of seven selected CNN models: EfficientNetB0, EfficientNetV2B0, EfficientNetV2B0-21k, ResNetV1-50, ResNetV2-50, MobileNetV1, and MobileNetV2 with transfer learning. To implement each pre-trained CNN architecture, we deployed the corresponded feature vector available from the TensorFlowHub, integrating it with dropout and dense layers to form a complete CNN model. Our findings indicated that the EfficientNetV2B0-21k (0.72B Floating-Point Operations and 7.1 M parameters) outperformed other CNN models in the IDC grading task. Nevertheless, we discovered that practically all selected CNN models perform well in the IDC grading task, with an average balanced accuracy of 0.936 ± 0.0189 on the cross-validation set and 0.9308 ± 0.0211on the test set.
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