Detail publikace

Deep Learning-based Classification of Viruses Using Transmission Electron Microscopy Images

ALI, M. JOSHI, R. DUTTA, M. BURGET, R. MEZINA, A.

Originální název

Deep Learning-based Classification of Viruses Using Transmission Electron Microscopy Images

Anglický název

Deep Learning-based Classification of Viruses Using Transmission Electron Microscopy Images

Jazyk

en

Originální abstrakt

Humans have a strong urge to categorize natural organisms, and the categorization of viruses becomes more challenging. Viruses are not visible with the naked eyes, and their automatic classification based on images obtained with Transmission Electron Microscopy (TEM) can help a lot in the medical field. Their classification is more challenging due to their complicated intracellular structures and lighting conditions to capture the TEM images. The proposed architecture has been developed for the classification of the 14 different types of viruses. The dataset has been split into the training set, validation set and test set. The proposed model obtained better experimental results with 96.90% classification accuracy on the validation set and 96.10 % on the test set of unseen images. The performance of the proposed model has been compared with state-of-the-art pre-trained deep-learning models such that XceptionNet, MobileNet and DenseNet201. The model is accurate and computationally less complex, which supports faster processing suitable for microscopic cell image analysis for different medical applications.

Anglický abstrakt

Humans have a strong urge to categorize natural organisms, and the categorization of viruses becomes more challenging. Viruses are not visible with the naked eyes, and their automatic classification based on images obtained with Transmission Electron Microscopy (TEM) can help a lot in the medical field. Their classification is more challenging due to their complicated intracellular structures and lighting conditions to capture the TEM images. The proposed architecture has been developed for the classification of the 14 different types of viruses. The dataset has been split into the training set, validation set and test set. The proposed model obtained better experimental results with 96.90% classification accuracy on the validation set and 96.10 % on the test set of unseen images. The performance of the proposed model has been compared with state-of-the-art pre-trained deep-learning models such that XceptionNet, MobileNet and DenseNet201. The model is accurate and computationally less complex, which supports faster processing suitable for microscopic cell image analysis for different medical applications.

Dokumenty

BibTex 


@inproceedings{BUT180406,
  author="Mohd Mohsin {Ali} and Rakesh Chandra {Joshi} and Malay Kishore {Dutta} and Radim {Burget} and Anzhelika {Mezina}",
  title="Deep Learning-based Classification of Viruses Using Transmission Electron Microscopy Images",
  annote="Humans have a strong urge to categorize natural organisms, and the categorization of viruses becomes more challenging. Viruses are not visible with the naked eyes, and their automatic classification based on images obtained with Transmission Electron Microscopy (TEM) can help a lot in the medical field. Their classification is more challenging due to their complicated intracellular structures and lighting conditions to capture the TEM images. The proposed architecture has been developed for the classification of the 14 different types of viruses. The dataset has been split into the training set, validation set and test set. The proposed model obtained better experimental results with 96.90% classification accuracy on the validation set and 96.10 % on the test set of unseen images. The performance of the proposed model has been compared with state-of-the-art pre-trained deep-learning models such that XceptionNet, MobileNet and DenseNet201. The model is accurate and computationally less complex, which supports faster processing suitable for microscopic cell image analysis for different medical applications.",
  address="IEEE",
  booktitle="45th International Conference on Telecommunications and Signal Processing (TSP 2022). IEEE, 2022",
  chapter="180406",
  doi="10.1109/TSP55681.2022.9851305",
  howpublished="online",
  institution="IEEE",
  year="2022",
  month="july",
  pages="174--178",
  publisher="IEEE",
  type="conference paper"
}