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Ensemble Deep-Learning-Enabled Clinical Decision Support System for Breast Cancer Diagnosis and Classification on Ultrasound Images
Open accessAbstract
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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“| Ragab et al. [101] | Segmented regions from original | ultrasound images Ultrasound Images | - | 96.92% (ACC) |”
From A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis (2024) · cites this paper as [101]
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A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis
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Metadata
- Authors
- Ragab, M., Albukhari, A., Alyami, J., & Mansour, R. F.
- Affiliations
- Al-Azhar University (EG)
- King Abdulaziz University (SA)
- New Valley University (EG)
- Corresponding author
- Ragab, Mahmoud
- Venue
- Biology
- Publisher
- MDPI AG
- Published
- 2022-03-14
- Volume
- 11
- Issue
- 3
- Pages
- 439
- ISSN
- 2079-7737 (ISSN-L)
- PMC
- PMC8945718
- OpenAlex
- W4221086517
- Cited by
- 172
- References
- 26
- Citations by year
- 2026: 23 · 2025: 32 · 2024: 46 · 2023: 46 · 2022: 24
- FWCI
- 12.18
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Biology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- King Abdulaziz University (D-000-000-0000)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- breast cancer diagnosis, breast ultrasound images, computer-aided diagnosis, clinical decision support systems, ensemble deep learning, Kapur's entropy, image segmentation, VGG16, VGG19, SqueezeNet, cat swarm optimization, multilayer perceptron, Wiener filter, contrast enhancement
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