ArticleScientific reports2022
TOPSIS aided ensemble of CNN models for screening COVID-19 in chest X-ray images.
Article in Scientific reports, 2022. 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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7 citing papers in PubMed.
- Fusing global context with multiscale context for enhanced breast cancer classification.Scientific reports · 2024Article
- Generalizable disease detection using model ensemble on chest X-ray images.Scientific reports · 2024Article
- MENet: A Mitscherlich function based ensemble of CNN models to classify lung cancer using CT scans.PloS one · 2024Article
- Scoping Review of Deep Learning Techniques for Diagnosis, Drug Discovery, and Vaccine Development in Leishmaniasis.Transboundary and emerging diseases · 2024Article
- Microstructural segmentation using a union of attention guided U-Net models with different color transformed images.Scientific reports · 2023Article
- Article
- Monkeypox detection from skin lesion images using an amalgamation of CNN models aided with Beta function-based normalization scheme.PloS one · 2023Article
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5 authors.
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Abstract
The novel coronavirus (COVID-19), has undoubtedly imprinted our lives with its deadly impact. Early testing with isolation of the individual is the best possible way to curb the spread of this deadly virus. Computer aided diagnosis (CAD) provides an alternative and cheap option for screening of the said virus. In this paper, we propose a convolution neural network (CNN)-based CAD method for COVID-19 and pneumonia detection from chest X-ray images. We consider three input types for three identical base classifiers. To capture maximum possible complementary features, we consider the original RGB image, Red channel image and the original image stacked with Robert's edge information. After that we develop an ensemble strategy based on the technique for order preference by similarity to an ideal solution (TOPSIS) to aggregate the outcomes of base classifiers. The overall framework, called TOPCONet, is very light in comparison with standard CNN models in terms of the number of trainable parameters required. TOPCONet achieves state-of-the-art results when evaluated on the three publicly available datasets: (1) IEEE COVID-19 dataset + Kaggle Pneumonia Dataset, (2) Kaggle Radiography dataset and (3) COVIDx.
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