ArticleBiology2022
Ensemble Deep-Learning-Enabled Clinical Decision Support System for Breast Cancer Diagnosis and Classification on Ultrasound Images.
Article in Biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers.
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Who cites it
50 citing papers in PubMed, 155 citations in OpenAlex.
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- Optimizing breast cancer classification based on cat swarm-enhanced ensemble neural network approach for improved diagnosis and treatment decisions.Scientific reports · 2025Article
- Computer-Aided Diagnosis of Equine Pharyngeal Lymphoid Hyperplasia Using the Object Detection-Based Processing Technique of Digital Endoscopic Images.Animals : an open access journal from MDPI · 2025Article
- Visual analysis of research hot topics and trends of clinical decision support system based on CiteSpace.Langenbeck's archives of surgery · 2025Review
- Variational mode directed deep learning framework for breast lesion classification using ultrasound imaging.Scientific reports · 2025Article
- SGA-Driven feature selection and random forest classification for enhanced breast cancer diagnosis: A comparative study.Scientific reports · 2025Article
- Feature Selection in Breast Cancer Gene Expression Data Using KAO and AOA with SVM Classification.Journal of medical systems · 2025Article
- Ultrasound-based deep learning radiomics for multi-stage assisted diagnosis in reducing unnecessary biopsies of BI-RADS 4A lesions.Quantitative imaging in medicine and surgery · 2025Article
- LightweightUNet: Multimodal Deep Learning with GAN-Augmented Imaging Data for Efficient Breast Cancer Detection.Bioengineering (Basel, Switzerland) · 2025Article
- Innovative breast cancer detection using a segmentation-guided ensemble classification framework.Biomedical engineering letters · 2025Article
- CNN-Based Cross-Modality Fusion for Enhanced Breast Cancer Detection Using Mammography and Ultrasound.Tomography (Ann Arbor, Mich.) · 2024Article
- Transforming Cancer Classification: The Role of Advanced Gene Selection.Diagnostics (Basel, Switzerland) · 2024Article
- Emerging research trends in artificial intelligence for cancer diagnostic systems: A comprehensive review.Heliyon · 2024Review
- The Impact of Artificial Intelligence on Microbial Diagnosis.Microorganisms · 2024Review
- Machine learning and new insights for breast cancer diagnosis.The Journal of international medical research · 2024Review
- Association between sociodemographic factors and health beliefs related to breast cancer screening behavior among Northern Thai women: a hospital-based study.Scientific reports · 2024Article
- A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis.Bioengineering (Basel, Switzerland) · 2024Review
- An Enhanced LightGBM-Based Breast Cancer Detection Technique Using Mammography Images.Diagnostics (Basel, Switzerland) · 2024Article
- AI-based automated breast cancer segmentation in ultrasound imaging based on Attention Gated Multi ResU-Net.PeerJ. Computer science · 2024Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
Abstract
Clinical Decision Support Systems (CDSS) provide an efficient way to diagnose the presence of diseases such as breast cancer using ultrasound images (USIs). Globally, breast cancer is one of the major causes of increased mortality rates among women. Computer-Aided Diagnosis (CAD) models are widely employed in the detection and classification of tumors in USIs. The CAD systems are designed in such a way that they provide recommendations to help radiologists in diagnosing breast tumors and, furthermore, in disease prognosis. The accuracy of the classification process is decided by the quality of images and the radiologist's experience. The design of Deep Learning (DL) models is found to be effective in the classification of breast cancer. In the current study, an Ensemble Deep-Learning-Enabled Clinical Decision Support System for Breast Cancer Diagnosis and Classification (EDLCDS-BCDC) technique was developed using USIs. The proposed EDLCDS-BCDC technique was intended to identify the existence of breast cancer using USIs. In this technique, USIs initially undergo pre-processing through two stages, namely wiener filtering and contrast enhancement. Furthermore, Chaotic Krill Herd Algorithm (CKHA) is applied with Kapur's entropy (KE) for the image segmentation process. In addition, an ensemble of three deep learning models, VGG-16, VGG-19, and SqueezeNet, is used for feature extraction. Finally, Cat Swarm Optimization (CSO) with the Multilayer Perceptron (MLP) model is utilized to classify the images based on whether breast cancer exists or not. A wide range of simulations were carried out on benchmark databases and the extensive results highlight the better outcomes of the proposed EDLCDS-BCDC technique over recent methods.
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Registered trials
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 Socratic graph.