ArticleBMC medical imaging2025
Effective BCDNet-based breast cancer classification model using hybrid deep learning with VGG16-based optimal feature extraction.
Article in BMC medical imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- A Novel Deep Learning Framework for Nipple Segmentation in Digital Mammography.Journal of imaging informatics in medicine · 2026Article
- A multi-scale adaptive filtering and AtRes_SRU-transformer synergy for breast cancer histopathology classification.Scientific reports · 2026Article
- FGOOPNet: A Fuzzy Deep Learning Approach for Mammographic Breast Cancer ClassificationCurrent medical imaging · 2026Article
- A hybrid deep learning approach with progressive cyclical CNN and firebug swarm optimization for breast cancer detection.PeerJ. Computer science · 2025Article
- Optimised Convolution Layers of DnCNN using Vedic Multiplier and Hyperparameter Tuning in Cancer Detection on Field Programmable Gate ArrayCurrent medical imaging · 2025Article
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4 authors.
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Abstract
problemBreast cancer is a leading cause of death among women, and early detection is crucial for improving survival rates. The manual breast cancer diagnosis utilizes more time and is subjective. Also, the previous CAD models mostly depend on manmade visual details that are complex to generalize across ultrasound images utilizing distinct techniques. Distinct imaging tools have been utilized in previous works such as mammography and MRI. However, these imaging tools are costly and less portable than ultrasound imaging. Also, ultrasound imaging is a non-invasive method commonly used for breast cancer screening. Hence, the paper presents a novel deep learning model, BCDNet, for classifying breast tumors as benign or malignant using ultrasound images.
aimThe primary aim of the study is to design an effective breast cancer diagnosis model that can accurately classify tumors in their early stages, thus reducing mortality rates. The model aims to optimize the weight and parameters using the RPAOSM-ESO algorithm to enhance accuracy and minimize false negative rates.
methodsThe BCDNet model utilizes transfer learning from a pre-trained VGG16 network for feature extraction and employs an AHDNAM classification approach, which includes ASPP, DTCN, 1DCNN, and an attention mechanism. The RPAOSM-ESO algorithm is used to fine-tune the weights and parameters.
resultsThe RPAOSM-ESO-BCDNet-based breast cancer diagnosis model provided 94.5 accuracy rates. This value is relatively higher than the previous models such as DTCN (88.2), 1DCNN (89.6), MobileNet (91.3), and ASPP-DTC-1DCNN-AM (93.8). Hence, it is guaranteed that the designed RPAOSM-ESO-BCDNet produces relatively accurate solutions for the classification than the previous models.
conclusionThe BCDNet model, with its sophisticated feature extraction and classification techniques optimized by the RPAOSM-ESO algorithm, shows promise in accurately classifying breast tumors using ultrasound images. The study suggests that the model could be a valuable tool in the early detection of breast cancer, potentially saving lives and reducing the burden on healthcare systems.
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