Application of automatic image analysis using a Deep Learning Neural Network for assessing the growth of green algae containing carotenoids – importance for environment, health and aquaculture
Artykuł w czasopiśmie
MNiSW
100
Lista 2024
| Status: | |
| Autorzy: | Zdeb Monika, Walo Mateusz, Łagód Grzegorz |
| Dyscypliny: | |
| Aby zobaczyć szczegóły należy się zalogować. | |
| Rok wydania: | 2025 |
| Wersja dokumentu: | Drukowana | Elektroniczna |
| Język: | angielski |
| Numer czasopisma: | 1 |
| Wolumen/Tom: | 32 |
| Strony: | 157 - 162 |
| Impact Factor: | 1,2 |
| Web of Science® Times Cited: | 1 |
| Scopus® Cytowania: | 0 |
| Bazy: | Web of Science | Scopus |
| Efekt badań statutowych | NIE |
| Finansowanie: | The research was carried out as part of the task commissioned under the title “Politechnical Network VIA CARPATIA named after the President of the Republic of Poland Lech Kaczyński” financed from the Special Purpose Grant of the Minister of Science, contract number MEiN/2022/ DPI/2578 action “PO SĄSIEDZKU - inter-university research internships and study visits. |
| Materiał konferencyjny: | NIE |
| Publikacja OA: | TAK |
| Licencja: | |
| Sposób udostępnienia: | Otwarte czasopismo |
| Wersja tekstu: | Ostateczna wersja opublikowana |
| Czas opublikowania: | W momencie opublikowania |
| Data opublikowania w OA: | 6 marca 2025 |
| Abstrakty: | angielski |
| Using deep learning and neural networks enables us to greatly speed-up quantitative studies and provide a useful tool for analyzing microscopic images. Studies conducted on selected algae Haematococcus and Coelastrum sp. confirm the feasibility of using the deep learning neural network. The confusion matrix demonstrated the numbers of errors generated by the YOLO v8 network in relation to the validation dataset. It indicated a higher number of errors in the detection of Haematococcus than Coleastrum. The F1 score, as the harmonic mean of precision and recall, is significantly higher for the class Coelastrum sp. than for Haematococcus sp. Machine learning can be applied not only to the detection of individual cells, but also to the detection of colonies over a wide range of sizes. This article discussed the technical and practical aspects of implementing these advanced methods and highlighted their importance in the aquaculture, food, medical, sustainable energy, and environmental sectors. |
