TY - GEN
T1 - Detection and Classification of Skin Cancer Using YOLOv8n
AU - Riyadi, Munawar A.
AU - Ayuningtias, Adela
AU - Isnanto, R. Rizal
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Skin cancer is a disease caused by the growth of abnormal cells in skin tissues. The World Health Organization (WHO) has recorded an 88% increase in deaths due to skin cancer caused by exposure to ultraviolet rays. Currently, in the medical field, the diagnosis of skin cancer involves a biopsy process, which requires considerable time and cost. Therefore, this study aims to develop a system for detecting and classifying skin cancer based on the shape of skin lesions using the You Only Look Once version 8 nano (YOLOv8n), which can detect lesions rapidly. The dataset used is ISIC 2019, comprising of 4289 images of cancerous skin lesions divided into 9 classes: Basal Cell Carcinoma, Squamous Cell Carcinoma, Melanoma, Actinic Keratosis, Dermatofibroma, Nevus, Seborrheic Keratosis, Pigmented Benign Keratosis, and Vascular Lesion. Experimental results show that the designed system performs well in detecting and classifying the lesions, achieving an overall accuracy of 93.5%, with a Precision of 93.5%, Recall of 93.7%, and an F1-Score of 93.5%.
AB - Skin cancer is a disease caused by the growth of abnormal cells in skin tissues. The World Health Organization (WHO) has recorded an 88% increase in deaths due to skin cancer caused by exposure to ultraviolet rays. Currently, in the medical field, the diagnosis of skin cancer involves a biopsy process, which requires considerable time and cost. Therefore, this study aims to develop a system for detecting and classifying skin cancer based on the shape of skin lesions using the You Only Look Once version 8 nano (YOLOv8n), which can detect lesions rapidly. The dataset used is ISIC 2019, comprising of 4289 images of cancerous skin lesions divided into 9 classes: Basal Cell Carcinoma, Squamous Cell Carcinoma, Melanoma, Actinic Keratosis, Dermatofibroma, Nevus, Seborrheic Keratosis, Pigmented Benign Keratosis, and Vascular Lesion. Experimental results show that the designed system performs well in detecting and classifying the lesions, achieving an overall accuracy of 93.5%, with a Precision of 93.5%, Recall of 93.7%, and an F1-Score of 93.5%.
KW - Skin Cancer
KW - Skin Lesions
KW - YOLOv8n
KW - classification method
UR - https://www.scopus.com/pages/publications/85214649107
U2 - 10.1109/EECSI63442.2024.10776505
DO - 10.1109/EECSI63442.2024.10776505
M3 - Conference contribution
AN - SCOPUS:85214649107
T3 - International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)
SP - 9
EP - 15
BT - Proceedings - 2024 11th International Conference on Electrical Engineering, Computer Science and Informatics, EECSI 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 11th International Conference on Electrical Engineering, Computer Science and Informatics, EECSI 2024
Y2 - 26 September 2024 through 27 September 2024
ER -
