Image-based macroscopic classification of Aspergillus fungi species using convolutional neural networks
College
Gokongwei College of Engineering
Department/Unit
Manufacturing Engineering and Management
Document Type
Conference Proceeding
Source Title
2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)
First Page
1
Last Page
4
Publication Date
2020
Abstract
This paper presents a technique for macroscopic classification of Aspergillus fungi species. The Aspergillus genus have several species that can be used in agricultural and medical applications. An automated process of macroscopic identification and classification of such species is described here. The scope of the study includes a 9-type Aspergillus fungi species. The learning mechanism used is a simple convolutional neural network. Using a total of 4545 macroscopic images, the model achieved a 90.06% accuracy in training, and 96.43% accuracy in validation.
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Recommended Citation
Billones, R. C., Calilung, E. J., Dadios, E. P., & Santiago, N. (2020). Image-based macroscopic classification of Aspergillus fungi species using convolutional neural networks. 2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), 1-4. Retrieved from https://animorepository.dlsu.edu.ph/faculty_research/14648
Keywords
Aspergillus—Identification; Identification—Equipment and supplies
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