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Convolutional Neural Networks for C. Elegans Muscle Age Classification Using Only Self-Learned Features
Abstract
Nematodes Caenorhabditis elegans (C. elegans) have been used as model organisms in a wide variety of biological studies, especially those intended to obtain a better understanding of aging and age-associated diseases. This paper focuses on automating the analysis of C. elegans imagery to classify the muscle age of nematodes based on the known and well established IICBU dataset. Unlike many modern classification methods, the proposed approach relies on deep learning techniques, specifically on convolutional neural networks (CNNs), to solve the problem and achieve high classification accuracy by focusing on non-handcrafted self-learned features. Various networks known from the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) have been investigated and adapted for the purposes of the C. elegans muscle aging dataset by applying transfer learning and data augmentation techniques. The proposed approach of unfreezing different numbers of convolutional layers at the feature extraction stage and introducing different structures of newly trained fully connected layers at the classification stage, enable to better fine-tune the selected networks. The adjusted CNNs, as featured in this paper, have been compared with other state-of-art methods. In anti-aging drug research, the proposed CNNs would serve as a very fast and effective age determination method, thus leading to reductions in time and costs of laboratory research.
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- Publication version
- Accepted or Published Version
- DOI:
- Digital Object Identifier (open in new tab) 10.26636/jtit.2022.165322
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- Category:
- Articles
- Type:
- artykuły w czasopismach
- Published in:
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Journal of Telecommunications and Information Technology
pages 84 - 96,
ISSN: 1509-4553 - Language:
- English
- Publication year:
- 2022
- Bibliographic description:
- Czaplewski B., Dzwonkowski M., Panas D.: Convolutional Neural Networks for C. Elegans Muscle Age Classification Using Only Self-Learned Features// Journal of Telecommunications and Information Technology -,iss. 4 (2022), s.84-96
- DOI:
- Digital Object Identifier (open in new tab) 10.26636/jtit.2022.165322
- Sources of funding:
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- COST_FREE
- Verified by:
- Gdańsk University of Technology
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