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Cited by 172AI in cancer detectionMedical Image Segmentation TechniquesRadiomics and Machine Learning in Medical Imaging

The DOI matches its Crossref record; 25 of the 26 references it cites were found in a registry or an index.

Ensemble Deep-Learning-Enabled Clinical Decision Support System for Breast Cancer Diagnosis and Classification on Ultrasound Images

Open access
Ragab, Mahmoud, Albukhari, Ashwag, Alyami, Jaber, Mansour, Romany F.·2022·Biology
DOI 10.3390/biology11030439PMC PMC8945718
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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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Crossref · 10.3390/biology11030439

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Figures

Figure 1 Overall process of the EDLCDS-BCDC technique.
Figure 1 Overall process of the EDLCDS-BCDC technique.CC BY · PubMed Central
Figure 2 SqueezeNet architecture.
Figure 2 SqueezeNet architecture.CC BY · PubMed Central
Figure 3 Sample and ground truth images (benign/malignant/normal).
Figure 3 Sample and ground truth images (benign/malignant/normal).CC BY · PubMed Central
Figure 4 Histogram of the images.
Figure 4 Histogram of the images.CC BY · PubMed Central
Figure 5 Sample visualization results: (a) original image; (b) noise-removed image, and (c) contrast-enhanced image.
Figure 5 Sample visualization results: (a) original image; (b) noise-removed image, and (c) contrast-enhanced image.CC BY · PubMed Central
Figure 6 Accuracy analysis results for the EDLCDS-BCDC technique.
Figure 6 Accuracy analysis results for the EDLCDS-BCDC technique.CC BY · PubMed Central
Figure 7 Loss graph analysis for the EDLCDS-BCDC technique.
Figure 7 Loss graph analysis for the EDLCDS-BCDC technique.CC BY · PubMed Central
Figure 8 ROC analysis results for the EDLCDS-BCDC technique under distinct epochs.
Figure 8 ROC analysis results for the EDLCDS-BCDC technique under distinct epochs.CC BY · PubMed Central
Figure 9 Comparative analysis of the EDLCDS-BCDC technique with recent methods.
Figure 9 Comparative analysis of the EDLCDS-BCDC technique with recent methods.CC BY · PubMed Central
Figure 10 Accuracy analysis of the EDLCDS-BCDC technique compared with recent methods.
Figure 10 Accuracy analysis of the EDLCDS-BCDC technique compared with recent methods.CC BY · PubMed Central

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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
ORCID
Venue
Biology
Publisher
MDPI AG
Published
2022-03-14
Volume
11
Issue
3
Pages
439
ISSN
2079-7737 (ISSN-L)
DOI
10.3390/biology11030439
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
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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