ArticleComputational intelligence and neuroscience2022
Classification of Breast Cancer Histopathological Images Using DenseNet and Transfer Learning.
Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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21 citing papers in PubMed.
- Dual-discriminator network-based classification method for breast ultrasound imaging.Quantitative imaging in medicine and surgery · 2026Article
- Vision-Language Models in Medical Imaging for Cancer Diagnosis: A Bibliometric Review.Bioengineering (Basel, Switzerland) · 2026Review
- A Hybrid Multimodal Cancer Diagnostic Framework Integrating Deep Learning of Histopathology and Whispering Gallery Mode Optical Sensors.Diagnostics (Basel, Switzerland) · 2026Article
- Breast cancer inter-image dissimilarity by feature optimization: An application of novel flea optimization algorithm.PloS one · 2026Article
- Deep-learning based model for sperm morphology assessment using the SMD/MSS dataset.Future science OA · 2025Article
- Deep Learning for the Early Detection of Invasive Ductal Carcinoma in Histopathological Images: Convolutional Neural Network Approach With Transfer Learning.JMIR formative research · 2025Article
- Deep learning model for grading carcinoma with Gini-based feature selection and linear production-inspired feature fusion.Scientific reports · 2025Article
- Domain knowledge-infused pre-trained deep learning models for efficient white blood cell classification.Scientific reports · 2025Article
- Using mathematical modelling and AI to improve delivery and efficacy of therapies in cancer.Nature reviews. Cancer · 2025Review
- Non-parametric prediction of brain MRI microstructure using transfer learning.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Patho-Net: enhancing breast cancer classification using deep learning and explainable artificial intelligence.American journal of cancer research · 2025Article
- Fusing global context with multiscale context for enhanced breast cancer classification.Scientific reports · 2024Article
- HI-Net: A novel histopathologic image segmentation model for metastatic breast cancer via lightweight dataset construction.Heliyon · 2024Article
- Article
- Breast Tumor Tissue Image Classification Using Single-Task Meta Learning with Auxiliary Network.Cancers · 2024Article
- Color-CADx: a deep learning approach for colorectal cancer classification through triple convolutional neural networks and discrete cosine transform.Scientific reports · 2024Article
- 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
- Canine Mammary Tumor Histopathological Image Classification via Computer-Aided Pathology: An Available Dataset for Imaging Analysis.Animals : an open access journal from MDPI · 2023Article
- Accuracy Analysis of Deep Learning Methods in Breast Cancer Classification: A Structured Review.Diagnostics (Basel, Switzerland) · 2023Review
- Article
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8 authors.
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Abstract
Breast cancer is one of the most common invading cancers in women. Analyzing breast cancer is nontrivial and may lead to disagreements among experts. Although deep learning methods achieved an excellent performance in classification tasks including breast cancer histopathological images, the existing state-of-the-art methods are computationally expensive and may overfit due to extracting features from in-distribution images. In this paper, our contribution is mainly twofold. First, we perform a short survey on deep-learning-based models for classifying histopathological images to investigate the most popular and optimized training-testing ratios. Our findings reveal that the most popular training-testing ratio for histopathological image classification is 70%: 30%, whereas the best performance (e.g., accuracy) is achieved by using the training-testing ratio of 80%: 20% on an identical dataset. Second, we propose a method named DenTnet to classify breast cancer histopathological images chiefly. DenTnet utilizes the principle of transfer learning to solve the problem of extracting features from the same distribution using DenseNet as a backbone model. The proposed DenTnet method is shown to be superior in comparison to a number of leading deep learning methods in terms of detection accuracy (up to 99.28% on BreaKHis dataset deeming training-testing ratio of 80%: 20%) with good generalization ability and computational speed. The limitation of existing methods including the requirement of high computation and utilization of the same feature distribution is mitigated by dint of the DenTnet.
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