ArticleMultidimensional systems and signal processing2021
Diagnosis of breast cancer based on modern mammography using hybrid transfer learning.
Article in Multidimensional systems and signal processing, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed.
- AI-Driven Breast Cancer Diagnosis: A Systematic Review of Imaging Modalities, Deep Learning, and Explainability.Cancers · 2026Review
- On the transfer learning behavior of domain-specific vision-language models in screening mammography.Journal of biomedical informatics · 2026Article
- HMC-net: a ResNet fused hierarchical multi-scale cross-attention architecture for mammographic breast malignancy recognition incorporating explainable AI.Frontiers in oncology · 2026Article
- TransBreastNet a CNN transformer hybrid deep learning framework for breast cancer subtype classification and temporal lesion progression analysis.Scientific reports · 2025Article
- A Lightweight Breast Cancer Mass Classification Model Utilizing Simplified Swarm Optimization and Knowledge Distillation.Bioengineering (Basel, Switzerland) · 2025Article
- A quantum-optimized approach for breast cancer detection using SqueezeNet-SVM.Scientific reports · 2025Article
- MammoViT: A Custom Vision Transformer Architecture for Accurate BIRADS Classification in Mammogram Analysis.Diagnostics (Basel, Switzerland) · 2025Article
- Segmentation for mammography classification utilizing deep convolutional neural network.BMC medical imaging · 2024Article
- CystNet: An AI driven model for PCOS detection using multilevel thresholding of ultrasound images.Scientific reports · 2024Article
- Optimizing Gene Selection and Cancer Classification with Hybrid Sine Cosine and Cuckoo Search Algorithm.Journal of medical systems · 2024Article
- Offloading the computational complexity of transfer learning with generic features.PeerJ. Computer science · 2024Article
- Breast Cancer Diagnosis Using Virtualization and Extreme Learning Algorithm Based on Deep Feed Forward Networks.Biomedical engineering and computational biology · 2024Article
- Presegmenter Cascaded Framework for Mammogram Mass Segmentation.International journal of biomedical imaging · 2024Article
- Mammography with deep learning for breast cancer detection.Frontiers in oncology · 2024Review
- ExpHBA Deep-IoT: Exponential Honey Badger Optimized Deep Learning For Breast Cancer Detection in IoT Healthcare System.Journal of digital imaging · 2023Article
- Fabrication of a microfluidic device for probiotic drug's dosage screening: Precision Medicine for Breast Cancer Treatment.Translational oncology · 2023Article
- Classification of Breast Cancer Using Transfer Learning and Advanced Al-Biruni Earth Radius Optimization.Biomimetics (Basel, Switzerland) · 2023Article
- Developing a Supplementary Diagnostic Tool for Breast Cancer Risk Estimation Using Ensemble Transfer Learning.Diagnostics (Basel, Switzerland) · 2023Article
- Detection and Classification of Histopathological Breast Images Using a Fusion of CNN Frameworks.Diagnostics (Basel, Switzerland) · 2023Article
- Deep feature selection using local search embedded social ski-driver optimization algorithm for breast cancer detection in mammograms.Neural computing & applications · 2023Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer is a common cancer in women. Early detection of breast cancer in particular and cancer, in general, can considerably increase the survival rate of women, and it can be much more effective. This paper mainly focuses on the transfer learning process to detect breast cancer. Modified VGG (MVGG) is proposed and implemented on datasets of 2D and 3D images of mammograms. Experimental results showed that the proposed hybrid transfer learning model (a fusion of MVGG and ImageNet) provides an accuracy of 94.3%. On the other hand, only the proposed MVGG architecture provides an accuracy of 89.8%. So, it is precisely stated that the proposed hybrid pre-trained network outperforms other compared Convolutional Neural Networks. The proposed architecture can be considered as an effective tool for radiologists to decrease the false negative and false positive rates. Therefore, the efficiency of mammography analysis will be improved.
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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.