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Cited by 174AI in cancer detectionMRI in cancer diagnosisRadiomics and Machine Learning in Medical Imaging

The DOI matches its Crossref record; 49 of the 51 references it cites were found in a registry or an index.

Convolution Neural Network for Breast Cancer Detection and Classification Using Deep Learning

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Abunasser, Basem, AL-Hiealy, Mohammed Rasheed, Zaqout, Ihab, Abu-Naser, Samy·2023·Asian Pacific Journal of Cancer Prevention
DOI 10.31557/apjcp.2023.24.2.531PMC PMC10162639
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Abstract

OBJECTIVE: Early detection and precise diagnosis of breast cancer (BC) plays an essential part in enhancing the diagnosis and improving the breast cancer survival rate of patients from 30 to 50%. Through the advances of technology in healthcare, deep learning takes a significant role in handling and inspecting a great number of X-ray, MRI, CTR images. The aim of this study is to propose a deep learning model (BCCNN) to detect and classify breast cancers into eight classes: benign adenosis (BA), benign fibroadenoma (BF), benign phyllodes tumor (BPT), benign tubular adenoma (BTA), malignant ductal carcinoma (MDC), malignant lobular carcinoma (MLC), malignant mucinous carcinoma (MMC), and malignant papillary carcinoma (MPC). METHODS: Breast cancer MRI images were classified into BA, BF, BPT, BTA, MDC, MLC, MMC, and MPC using a proposed Deep Learning model with additional 5 fine-tuned Deep learning models consisting of Xception, InceptionV3, VGG16, MobileNet and ResNet50 trained on ImageNet database. The dataset was collected from Kaggle depository for breast cancer detection and classification. That Dataset was boosted using GAN technique. The images in the dataset have 4 magnifications (40X, 100X, 200X, 400X, and Complete Dataset). Thus we evaluated the proposed Deep Learning model and 5 pre-trained models using each dataset individually. That means we carried out a total of 30 experiments. The measurement that was used in the evaluation of all models includes: F1-score, recall, precision, accuracy. RESULTS: The classification F1-score accuracies of Xception, InceptionV3, ResNet50, VGG16, MobileNet, and Proposed Model (BCCNN) were 97.54%, 95.33%, 98.14%, 97.67%, 93.98%, and 98.28%, respectively. CONCLUSION: Dataset Boosting, preprocessing and balancing played a good role in enhancing the detection and classification of breast cancer of the proposed model (BCCNN) and the fine-tuned pre-trained models' accuracies greatly. The best accuracies were attained when the 400X magnification of the MRI images due to their high images resolution.

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Crossref · 10.31557/apjcp.2023.24.2.531

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Figures

Figure 1 Breast Cancer Histopathological Dataset (BreakHis) Organization
Figure 1 Breast Cancer Histopathological Dataset (BreakHis) OrganizationCC BY-NC · PubMed Central
Figure 2 Samples Images from 40X Datasets with Its Eight Classes
Figure 2 Samples Images from 40X Datasets with Its Eight ClassesCC BY-NC · PubMed Central
Figure 3 Shows the Structure of the Proposed Model BCCNN
Figure 3 Shows the Structure of the Proposed Model BCCNNCC BY-NC · PubMed Central
Figure 4 Shows the Structure of the Pre-Trained Model ResNet50
Figure 4 Shows the Structure of the Pre-Trained Model ResNet50CC BY-NC · PubMed Central
Figure 5 Shows the Structure of the Pre-Trained Model VGG16
Figure 5 Shows the Structure of the Pre-Trained Model VGG16CC BY-NC · PubMed Central
Figure 6 Shows the Structure of the Pre-Trained Model Xception
Figure 6 Shows the Structure of the Pre-Trained Model XceptionCC BY-NC · PubMed Central
Figure 7 Shows the Structure of the Pre-Trained Model InceptionV3
Figure 7 Shows the Structure of the Pre-Trained Model InceptionV3CC BY-NC · PubMed Central
Figure 8 (A) the overall MobileNet architecture and (B) an in-depth explanation of the Depth-wise Separable (DS) convolutions layer
Figure 8 (A) the overall MobileNet architecture and (B) an in-depth explanation of the Depth-wise Separable (DS) convolutions layerCC BY-NC · PubMed Central
Figure 9 Shows the Structure of the 30 Experiments Applied in This Study
Figure 9 Shows the Structure of the 30 Experiments Applied in This StudyCC BY-NC · PubMed Central
Figure 10 Training Accuracies for the 6 Models in the 5 Scenarios
Figure 10 Training Accuracies for the 6 Models in the 5 ScenariosCC BY-NC · PubMed Central
Figure 11 Validation Accuracies for the 6 Models in the 5 Scenarios
Figure 11 Validation Accuracies for the 6 Models in the 5 ScenariosCC BY-NC · PubMed Central
Figure 12 Testing Accuracies for the 6 Models in the 5 Scenarios
Figure 12 Testing Accuracies for the 6 Models in the 5 ScenariosCC BY-NC · PubMed Central
Figure 13 Training Loss for the 6 Models in the 5 Scenarios
Figure 13 Training Loss for the 6 Models in the 5 ScenariosCC BY-NC · PubMed Central
Figure 14 Validation Loss for the 6 Models in the 5 Scenarios
Figure 14 Validation Loss for the 6 Models in the 5 ScenariosCC BY-NC · PubMed Central
Figure 15 Testing Loss for the 6 Models in the 5 Scenarios
Figure 15 Testing Loss for the 6 Models in the 5 ScenariosCC BY-NC · PubMed Central
Figure 16 Time Performance for the 6 Models in the 5 Scenarios
Figure 16 Time Performance for the 6 Models in the 5 ScenariosCC BY-NC · PubMed Central
Figure 17 Precision for the 6 Models in the 5 Scenarios
Figure 17 Precision for the 6 Models in the 5 ScenariosCC BY-NC · PubMed Central
Figure 18 Recall for the 6 Models in the 5 Scenarios
Figure 18 Recall for the 6 Models in the 5 ScenariosCC BY-NC · PubMed Central
Figure 19 F1-Score for the 6 Models in the 5 Scenarios
Figure 19 F1-Score for the 6 Models in the 5 ScenariosCC BY-NC · PubMed Central

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Metadata

Authors
Abunasser, B., AL-Hiealy, M. R., Zaqout, I., & Abu-Naser, S.
Affiliations
  • University Malaysia of Computer Science and Engineering (MY)
  • Al-Azhar University – Gaza (PS)
Corresponding author
Abunasser, Basem
ORCID
Venue
Asian Pacific Journal of Cancer Prevention
Publisher
EpiSmart Science Vector Ltd
Published
2023-02-01
Volume
24
Issue
2
Pages
531-544
ISSN
  • 1513-7368 (ISSN-L)
  • 2476-762X
DOI
10.31557/apjcp.2023.24.2.531
PMC
PMC10162639
OpenAlex
W4322616751
Cited by
174
References
51
Citations by year
2026: 41 · 2025: 81 · 2024: 45 · 2023: 6
FWCI
16.31
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Asian Pacific Journal of Cancer Prevention (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › MRI in cancer diagnosis
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
Keywords
breast cancer, breast cancer classification, convolutional neural networks, pre-trained models, transfer learning, GAN data augmentation, benign breast tumors, ductal carcinoma, lobular carcinoma, papillary carcinoma, magnification, early detection
MeSH terms
Deep Learning, Breast Neoplasms, Carcinoma, Papillary, Female, Fibroma, Humans, Fibroadenoma, Carcinoma, Ductal, Breast, Carcinoma, Lobular

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