Stacking approach for accurate Invasive Ductal Carcinoma classification

The accurate diagnosis of Breast cancer (BC) requires adequately exploiting Artificial intelligence (AI)-based methods in the diagnosing process. To tackle the issue of accurate BC diagnosis, we have proposed a deep learning-based stacking method (StackBC). In particular, we have incorporated deep l...

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Bibliographic Details
Published in:Computers & electrical engineering 2022-05, Vol.100, p.107937, Article 107937
Main Authors: Haq, Amin Ul, Li, Jian Ping, Ali, Zafar, Khan, Inayat, Khan, Ajab, Uddin, M. Irfan, Agbley, Bless Lord Y., Khan, Riaz Ullah
Format: Article
Language:English
Subjects:
Accuracy
Analysis
Artificial intelligence
Artificial neural networks
Breast cancer
Classification
Classifiers
Clinical image data
Data augmentation
Deep learning
Diagnosis
Machine learning
Stacking
Transfer learning
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Summary:The accurate diagnosis of Breast cancer (BC) requires adequately exploiting Artificial intelligence (AI)-based methods in the diagnosing process. To tackle the issue of accurate BC diagnosis, we have proposed a deep learning-based stacking method (StackBC). In particular, we have incorporated deep learning models including Convolutional Neural Network (CNN), Long Short Term Memory (LSTM), and Gated Recurrent Unit (GRU) for the classification of Invasive Ductal Carcinoma (IDC). Additionally, Transfer Learning (TL) and Data Augmentation (DA) approaches have been incorporated to balance the dataset and adequately train the model. To further improve the predictive outputs of the model, we used the stacking technique. Among the three individual base classifiers, the performance of the GRU model was better. Hence, we selected the GRU as a meta classifier to distinguish between Non-IDC and IDC breast images. The experimental results confirmed that the StackBC method outperformed state-of-the-art methods. [Display omitted] •Breast cancer detection and diagnostic method called Stack Breast Cancer is proposed.•Our CNN, LSTM and GRU models were evaluated using a breast histology image dataset.•Transfer learning and data augmentation were used to improve the models’ performances.•Stacking the models produced an improved model with a GRU meta predictor and performance of Stack BC model were higher compared to existing models.
ISSN:0045-7906
1879-0755
DOI:10.1016/j.compeleceng.2022.107937