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Fusing compressed deep ConvNets with a self-normalizing residual block and alpha dropout for a cost-efficient classification and diagnosis of gastrointestinal tract diseases
The challenging task of diagnosing gastrointestinal (GI) tracts recently became a popular research topic, where most researchers performed extraordinary feats using numerous deep learning (DL) and computer vision techniques to achieve state-of-the-art (SOTA) diagnostic performance based on accuracy....
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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Elsevier
2022
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9677079/ https://www.ncbi.nlm.nih.gov/pubmed/36420314 http://dx.doi.org/10.1016/j.mex.2022.101925 |