Intelligent Hybrid Deep Learning Model for Breast Cancer Detection

Breast cancer (BC) is a type of tumor that develops in the breast cells and is one of the most common cancers in women. Women are also at risk from BC, the second most life-threatening disease after lung cancer. The early diagnosis and classification of BC are very important. Furthermore, manual det...

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Bibliographic Details
Published in:Electronics (Basel) 2022-09, Vol.11 (17), p.2767
Main Authors: Wang, Xiaomei, Ahmad, Ijaz, Javeed, Danish, Zaidi, Syeda Armana, Alotaibi, Fahad M, Ghoneim, Mohamed E, Daradkeh, Yousef Ibrahim, Asghar, Junaid, Eldin, Elsayed Tag
Format: Article
Language:English
Subjects:
Accuracy
Automation
Breast cancer
Cancer
Classification
Datasets
Deep learning
Diagnosis
Histopathology
Hybrid systems
Machine learning
Mammography
Medical research
Model testing
Neural networks
Sensitivity
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Summary:Breast cancer (BC) is a type of tumor that develops in the breast cells and is one of the most common cancers in women. Women are also at risk from BC, the second most life-threatening disease after lung cancer. The early diagnosis and classification of BC are very important. Furthermore, manual detection is time-consuming, laborious work, and, possibility of pathologist errors, and incorrect classification. To address the above highlighted issues, this paper presents a hybrid deep learning (CNN-GRU) model for the automatic detection of BC-IDC (+,−) using whole slide images (WSIs) of the well-known PCam Kaggle dataset. In this research, the proposed model used different layers of architectures of CNNs and GRU to detect breast IDC (+,−) cancer. The validation tests for quantitative results were carried out using each performance measure (accuracy (Acc), precision (Prec), sensitivity (Sens), specificity (Spec), AUC and F1-Score. The proposed model shows the best performance measures (accuracy 86.21%, precision 85.50%, sensitivity 85.60%, specificity 84.71%, F1-score 88%, while AUC 0.89 which overcomes the pathologist’s error and miss classification problem. Additionally, the efficiency of the proposed hybrid model was tested and compared with CNN-BiLSTM, CNN-LSTM, and current machine learning and deep learning (ML/DL) models, which indicated that the proposed hybrid model is more robust than recent ML/DL approaches.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics11172767