The Epidemiology of Skin Cancer at the National Hospital  of Dermatology and Venereology From 2017 - 2021

Hong, Son Nguyen; Huu, Quang Nguyen; Dinh, Quan Q; Nguyen, Binh Thanh; Dinh, Tam; Thanh, Hien Le; Manh, Tan Nguyen; Huu, Sau Nguyen; Hong, H.; Ha, Patrick K.

doi:10.56320/tcdlhvn.36.71

Cited by 1 publication

(1 citation statement)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Basal cell carcinoma was the most common type of skin cancer, followed by squamous cell carcinoma and melanoma. In addition, the majority of patients were over 60 years old, and there was an increase in the proportion of patients under 60 years old over the years [4].…”

Section: Introductionmentioning

confidence: 99%

EfficientSkinCaSV2B3: An Efficient Framework Towards Improving Skin Classification and Segmentation

Thanh,

Nguyen

2024

ijacsa

View full text Add to dashboard Cite

Ozone layer depletion has gained attention as a serious environmental issue. Because of its effects on human health especially skin cancer. Besides, Ultraviolet (UV) radiation is known to be a major risk factor for skin cancer. For instance, it can damage the DNA in skin cells leading to mutations that may eventually result in cancerous growth. Basal cell carcinoma, squamous cell carcinoma, and melanoma are the three primary forms of skin cancer linked to UV exposure. Additionally, it triggers associated illnesses including nevus, seborrheic keratosis, actinic keratosis, dermatofibroma, and vascular lesions. Many medical and computer studies were published as a result to address these disorders. Especially, using an aspect of deep learning that is transfer learning and fine-tuning for the classification of skin images. In this research, the EffecientSkinCaSV2B3 framework was proposed and applied to classify and segment the skin cancer dataset, which were collected and validated by The International Skin Imaging Collaboration (ISIC). In addition, Gradient-weighted Class Activation Mapping (Grad-CAM) is used in skin cancer classification to visually explain images, aiding in understanding model decisions and highlighting important areas. Based on color and texture, k-means clustering was used for the segmentation between portions that were healthy and those that were unhealthy. The study reached a surprising accuracy of 84.91% in nine classes of classifying skin cancer. In other experiments, the customized EfficientNetV2B3 model achieved 94.00% in classifying malign and benign. Moreover, scenarios pointed out that in classifying six classes (i.e., between benign skin diseases) and three classes (i.e., between malign skin diseases) the model earned a high accuracy of 89.56% and 96.74%, respectively.

show abstract

Section: Introductionmentioning

confidence: 99%