Year: 2022
Type: article
Abstract: Classification of skin cancer is a growing research topic with significant challenges in the image processing. Learning algorithms for classifying a kind of skin cancer have been presented in recent articles to accelerate the diagnosis process with a rapid and accurate diagnosis. However, effective detection of skin cancer requires extensive graphical data. Inspired by deep learning successful results in computer vision, A Convolutional Neural Network (CNN) is proposed in this study to build a skin cancer classification model. We conduct this experiment by collecting massive skin cancer datasets, conducting pre-processing, training models, and evaluating the performance. Based on the experiment result, the benign and malignant classification model can obtain a good accuracy with a slight loss. Therefore, the results obtained reached an accuracy of 54%.
Author: Marselina Endah H.
Institution: Universitas Respati Yogyakarta
Language: English
Subfield: Oncology
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W4289925587