American Psychological Association 6th edition

Abir, S. I., Shoha, S., Hossain, M. M., Rahman, S. M., Saimon, S. I., Islam, I., Mamun, M. A. I., & Khan, N. I. (2024). Deep Learning-Based Classification of Skin Lesions: Enhancing Melanoma Detection through Automated Preprocessing and Data Augmentation. Journal of Computer Science & Technology Studies, 6(5), 152-167. https://doi.org/10.32996/jcsts.2024.6.5.13

ISO-690 (author-date, English)

ABIR, Shake Ibna, SHOHA, Shaharina, HOSSAIN, Md Miraj, RAHMAN, Syed Moshiur, SAIMON, Shariar Islam, ISLAM, Intiser, MAMUN, Md Atikul Islam und KHAN, Nazrul Islam, 2024. Deep Learning-Based Classification of Skin Lesions: Enhancing Melanoma Detection through Automated Preprocessing and Data Augmentation. Journal of Computer Science & Technology Studies. 1 November 2024. Vol. 6, no. 5, p. 152-167. DOI 10.32996/jcsts.2024.6.5.13.

Modern Language Association 9th edition

Abir, S. I., S. Shoha, M. M. Hossain, S. M. Rahman, S. I. Saimon, I. Islam, M. A. I. Mamun, und N. I. Khan. „Deep Learning-Based Classification of Skin Lesions: Enhancing Melanoma Detection through Automated Preprocessing and Data Augmentation.“. Journal of Computer Science & Technology Studies, Bd. 6, Nr. 5, November 2024, S. 152-67, https://doi.org/10.32996/jcsts.2024.6.5.13.

Mohr Siebeck - Recht (Deutsch - Österreich)

Abir, Shake Ibna/Shoha, Shaharina/Hossain, Md Miraj/Rahman, Syed Moshiur/Saimon, Shariar Islam/Islam, Intiser u. a.: Deep Learning-Based Classification of Skin Lesions: Enhancing Melanoma Detection through Automated Preprocessing and Data Augmentation., Journal of Computer Science & Technology Studies 2024, 152-167.

Emerald - Harvard

Abir, S.I., Shoha, S., Hossain, M.M., Rahman, S.M., Saimon, S.I., Islam, I., Mamun, M.A.I. und Khan, N.I. (2024), „Deep Learning-Based Classification of Skin Lesions: Enhancing Melanoma Detection through Automated Preprocessing and Data Augmentation.“, Journal of Computer Science & Technology Studies, Vol. 6 No. 5, S. 152-167.

Achtung: Diese Zitate sind unter Umständen nicht zu 100% korrekt.