Deep learning-based classification of microalgae using light and scanning electron microscopy images
Yazarlar (5)
Mesut Ersin Sonmez Karamanoğlu Mehmetbey Üniversitesi, Türkiye
Betul Altinsoy Karamanoğlu Mehmetbey Üniversitesi, Türkiye
Betul Yilmaz Ozturk Eskişehir Osmangazi Üniversitesi, Türkiye
Numan Emre Gumus Karamanoğlu Mehmetbey Üniversitesi, Türkiye
Dr. Öğr. Üyesi Numan ECZACIOĞLU Karamanoğlu Mehmetbey Üniversitesi, Türkiye
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | Micron (Q1) | ||
| Dergi ISSN | 0968-4328 Dergi Bilgileri (2023) | ||
| Dergi Tarandığı Indeksler | SCI | ||
| Makale Dili | Türkçe | Basım Tarihi | 09-2023 |
| Cilt / Sayı / Sayfa | 172 / 1 / 103506–0 | DOI | 10.1016/j.micron.2023.103506 |
| Makale Linki | http://dx.doi.org/10.1016/j.micron.2023.103506 | ||
| UAK Araştırma Alanları |
Biyoteknoloji
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| Özet |
| Microalgae possess diverse applications, such as food production, animal feed, cosmetics, plastics manufacturing, and renewable energy sources. However, uncontrolled proliferation, known as algal bloom, can detrimentally impact ecosystems. Therefore, the accurate detection, monitoring, identification, and tracking of algae are imperative, albeit demanding considerable time, effort, and expertise, as well as financial resources. Deep learning, employing image pattern recognition, emerges as a practical and promising approach for rapid and precise microalgae cell counting and identification. In this study, we processed light microscopy (LM) and scanning electron microscopy (SEM) images of two Cyanobacteria species and three Chlorophyta species to classify them, utilizing state-of-the-art Convolutional Neural Network (CNN) models, including VGG16, MobileNet V2, Xception, NasnetMobile, and EfficientNetV2 ... |
| Anahtar Kelimeler |
| Microalgae | Species Classification | Deep learning |
Science Direct
Deep learning-based classification of microalgae using light and scanning electron microscopy images
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Yöksis | 15 |
| Web of Science | 29 |
| Scopus | 32 |
| Google Scholar | 45 |