Advanced search ?

Object

Convolutional Neural Networks for C. Elegans Muscle Age Classification Using Only Self-learned Features, Journal of Telecommunications and Information Technology, 2022, nr 4
Download

Title: Convolutional Neural Networks for C. Elegans Muscle Age Classification Using Only Self-learned Features, Journal of Telecommunications and Information Technology, 2022, nr 4

Creator:

Dzwonkowski, Mariusz ; Czaplewski, Bartosz ; Panas, Damian

Date:

2022, nr 4

Resource Type:

artykuł

Description:

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.

Publisher:

Instytut Łączności - Państwowy Instytut Badawczy

Format:

application/pdf

Resource Identifier:

oai:bc.itl.waw.pl:2257 ; ISSN 1509-4553, on-line: ISSN 1899-8852

DOI:

10.26636/jtit.2022.165322

eISSN:

1899-8852

Source:

Journal of Telecommunications and Information Technology

Language:

ang

Rights Management:

Biblioteka Naukowa Instytutu Łączności

Subject and keywords:

machine learning deep learning C. elegans muscle aging convolutional neural networks biomedical imaging

Object collections:

Last modified:

Jun 6, 2024

In our library since:

Feb 7, 2023

Number of object content hits:

24

All available object's versions:

https://ribes-54.man.poznan.pl/publication/2566

Show description in RDF format:

RDF

Show description in OAI-PMH format:

OAI-PMH

Edition name Date
Czaplewski, Bartosz, Convolutional Neural Networks for C. Elegans Muscle Age Classification Using Only Self-learned Features, Journal of Telecommunications and Information Technology, 2022, nr 4 Jun 6, 2024

Objects Similar

2024, nr 3, JTIT-artykuły
2024, nr 3, JTIT-artykuły
A Generalized Learning Approach to Deep Neural Networks, Journal of Telecommunications and Information Technology, 2024, nr 3

Ponti, Francesca Frezza, Fabrizio Simeoni, Patrizio Parisi, Raffaele

2024, nr 3
artykuł
2024, nr 3, JTIT-artykuły
2024, nr 3, JTIT-artykuły
A Collaborative Approach to Detecting DDoS Attacks in SDN Using Entropy and Deep Learning, Journal of Telecommunications and Information Technology, 2024, nr 3

Narayan, D. G. Heena, W Amit, K

2024, nr 3
artykuł
Digital Fingerprinting Based on Quaternion Encryption Scheme for Gray-Tone Images, Journal of Telecommunications and Information Technology, 2014, nr 2

Rykaczewski, Roman Dzwonkowski, Mariusz Czaplewski, Bartosz

2014, nr 2
artykuł
Detection of Monocrystalline Silicon Wafer Defects Using Deep Transfer Learning, Journal of Telecommunications and Information Technology, 2022, nr 1

Fauzi, Ahmad Hadinata Iskandar, D. N. F. Awang Ganum, Adriana Chin, Lim Phei

2022, nr 1
artykuł
Network Traffic Classification in an NFV Environment using Supervised ML Algorithms, Journal of Telecommunications and Information Technology, 2021, nr 3

Latkoski, Pero Ilievski, Gjorgji

2021, nr 3
artykuł
Manipulation of compressed data using MPEG-7 low level audio descriptors, Journal of Telecommunications and Information Technology, 2003, nr 2

Harders, Nick Perrow, Shane Stirling, David Lukasiak, Jason

2003, nr 2
artykuł
Contextual probability, Journal of Telecommunications and Information Technology, 2003, nr 3

Wang, Hui

2003, nr 3
artykuł
2023, nr 4, JTIT-artykuły
2023, nr 4, JTIT-artykuły
Designing Smart Antennas Using Machine Learning Algorithms, Journal of Telecommunications and Information Technology, 2023, nr 4

Samantaray, Barsa Das, Kunal Kumar Roy, Jibendu Sekhar

2023
artykuł
More
×

Citation

Citation style:

Contact details

Address

Instytut Łączności – Państwowy Instytut Badawczy ul. Szachowa 1 04-894 Warszawa

Phone

(+48) 5128 696

E-Mail

biblioteka@il-pib.pl

Visit us!

This page uses 'cookies'. More information