ArticleComplex & intelligent systems2021
Improving ductal carcinoma in situ classification by convolutional neural network with exponential linear unit and rank-based weighted pooling.
Article in Complex & intelligent systems, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 56 citations in OpenAlex.
- A review of application of Artificial Intelligence in breast cancer detection and treatment.Discover oncology · 2026Review
- A hybrid GAN-based deep learning framework for thermogram-based breast cancer detection.Scientific reports · 2025Article
- Deep learning-based risk stratification of ductal carcinomaFrontiers in oncology · 2025Article
- Fully Interpretable Deep Learning Model Using IR Thermal Images for Possible Breast Cancer Cases.Biomimetics (Basel, Switzerland) · 2024Article
- RETRACTED: Multi-modal deep learning methods for classification of chest diseases using different medical imaging and cough sounds.PloS one · 2024Article
- An Integrated Intelligent System for Breast Cancer Detection at Early Stages Using IR Images and Machine Learning Methods with Explainability.SN computer science · 2023Article
- PoolingBioMed research international · 2022Review
Corrections and comments
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Authors and funding
6 authors at 5 institutions in 5 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Ductal carcinoma in situ (DCIS) is a pre-cancerous lesion in the ducts of the breast, and early diagnosis is crucial for optimal therapeutic intervention. Thermography imaging is a non-invasive imaging tool that can be utilized for detection of DCIS and although it has high accuracy (~ 88%), it is sensitivity can still be improved. Hence, we aimed to develop an automated artificial intelligence-based system for improved detection of DCIS in thermographs. This study proposed a novel artificial intelligence based system based on convolutional neural network (CNN) termed CNN-BDER on a multisource dataset containing 240 DCIS images and 240 healthy breast images. Based on CNN, batch normalization, dropout, exponential linear unit and rank-based weighted pooling were integrated, along with L-way data augmentation. Ten runs of tenfold cross validation were chosen to report the unbiased performances. Our proposed method achieved a sensitivity of 94.08 ± 1.22%, a specificity of 93.58 ± 1.49 and an accuracy of 93.83 ± 0.96. The proposed method gives superior performance than eight state-of-the-art approaches and manual diagnosis. The trained model could serve as a visual question answering system and improve diagnostic accuracy.
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