ArticleComputational intelligence and neuroscience2021
Classification of Hematoxylin and Eosin-Stained Breast Cancer Histology Microscopy Images Using Transfer Learning with EfficientNets.
Article in Computational intelligence and neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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29 citing papers in PubMed, 72 citations in OpenAlex.
- MS-GATOR: multi-scale graph attention with topological reasoning for segmentation and classification of prostate cancer based on Gleason scores using histopathology images.Scientific reports · 2026Article
- A novel multimodal framework integrating pathomics, deep learning, and machine learning for breast cancer histological grades classification.Diagnostic pathology · 2026Article
- Mass spectrometric imaging of organic and metallic metabolites by plasmon-induced interfacial charge-transfer transition (PICTT) on Au sputtered ITO slides.Chemical science · 2025Article
- Leveraging an ensemble of EfficientNetV1 and EfficientNetV2 models for classification and interpretation of breast cancer histopathology images.Scientific reports · 2025Article
- Explainability of a Deep Learning-Based Classification Model for Antineutrophil Cytoplasmic Autoantibody-Associated Glomerulonephritis.Kidney international reports · 2025Article
- Phasor-FLIM and SHG imaging for quantitative analysis of lung cancer autofluorescence.Computational and structural biotechnology journal · 2025Article
- Publicly available datasets of breast histopathology H&E whole-slide images: A scoping review.Journal of pathology informatics · 2024Article
- Improving performance in colorectal cancer histology decomposition using deep and ensemble machine learning.Heliyon · 2024Article
- Evaluation of a Deep Learning Based Approach to Computational Label Free Cell Viability Quantification.bioRxiv : the preprint server for biology · 2024Article
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- Deep learning approaches for breast cancer detection in histopathology images: A review.Cancer biomarkers : section A of Disease markers · 2024Review
- Evaluating the effectiveness of stain normalization techniques in automated grading of invasive ductal carcinoma histopathological images.Scientific reports · 2023Article
- Parasitic egg recognition using convolution and attention network.Scientific reports · 2023Article
- Breast cancer histopathology image-based gene expression prediction using spatial transcriptomics data and deep learning.Scientific reports · 2023Article
- Article
- BRMI-Net: Deep Learning Features and Flower Pollination-Controlled Regula Falsi-Based Feature Selection Framework for Breast Cancer Recognition in Mammography Images.Diagnostics (Basel, Switzerland) · 2023Article
- Application of EfficientNet-B0 and GRU-based deep learning on classifying the colposcopy diagnosis of precancerous cervical lesions.Cancer medicine · 2023Article
- Article
- Performance analysis of seven Convolutional Neural Networks (CNNs) with transfer learning for Invasive Ductal Carcinoma (IDC) grading in breast histopathological images.Scientific reports · 2022Article
- An image classification deep-learning algorithm for shrapnel detection from ultrasound images.Scientific reports · 2022Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer is a fatal disease and is a leading cause of death in women worldwide. The process of diagnosis based on biopsy tissue is nontrivial, time-consuming, and prone to human error, and there may be conflict about the final diagnosis due to interobserver variability. Computer-aided diagnosis systems have been designed and implemented to combat these issues. These systems contribute significantly to increasing the efficiency and accuracy and reducing the cost of diagnosis. Moreover, these systems must perform better so that their determined diagnosis can be more reliable. This research investigates the application of the EfficientNet architecture for the classification of hematoxylin and eosin-stained breast cancer histology images provided by the ICIAR2018 dataset. Specifically, seven EfficientNets were fine-tuned and evaluated on their ability to classify images into four classes:
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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 OpenQuestion graph.