Optimized DenseNet Architectures for Precise Classification of Edible and Poisonous Mushrooms
Abstract Background The subtle differences between edible and toxic mushroom species make classification difficult. Traditional methods often result in errors which led to misclassifications and conventional machine learning models often struggle in feature extraction due to subtle differences in mu...
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| Autori principali: | , , , , |
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| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
Springer
2025-06-01
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| Serie: | International Journal of Computational Intelligence Systems |
| Soggetti: | |
| Accesso online: | https://doi.org/10.1007/s44196-025-00871-y |
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