GDL

Journal from gdlhub / 2021-09-10 15:57:38
By : Jairo Hurtado, Francisco Reales, Telkomnika
Created : 2021-09-10, with 0 files

Keyword : artificial intelligence, image processing, machine, Melanoma, skin cancer
Url : http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/20292
Document Source : Web

In the last years, computer vision systems for the detection of skin cancer have being proposed, specially using machine learning techniques for the classification of the disease and features based on the ABCD dermatology criterion, which gives information on the status of the skin lesion based on static properties such as geometry, color and texture, making it an appropriate criterion for medical diagnosis systems that work through images. This paper proposes a novel skin cancer classification system that works on images taken from a standard camera and studies the impact on the results of the smoothed bootstrapping, which was used to augment the original dataset. Eight classifiers with different topologies (KNN, ANN and SVM) were compared, with and without data augmentation, showing that the classifier with the highest performance as well as the must balanced one was the ANN with data augmentation, achieving an AUC of 87.1%, which saw an improvement from an AUC of 84.3% of the ANN trained with the original dataset.

Description Alternative :

In the last years, computer vision systems for the detection of skin cancer have being proposed, specially using machine learning techniques for the classification of the disease and features based on the ABCD dermatology criterion, which gives information on the status of the skin lesion based on static properties such as geometry, color and texture, making it an appropriate criterion for medical diagnosis systems that work through images. This paper proposes a novel skin cancer classification system that works on images taken from a standard camera and studies the impact on the results of the smoothed bootstrapping, which was used to augment the original dataset. Eight classifiers with different topologies (KNN, ANN and SVM) were compared, with and without data augmentation, showing that the classifier with the highest performance as well as the must balanced one was the ANN with data augmentation, achieving an AUC of 87.1%, which saw an improvement from an AUC of 84.3% of the ANN trained with the original dataset.

Give Comment ?#(0) | Bookmark

Property	Value
Publisher ID	gdlhub
Organization	Telkomnika
Contact Name	Herti Yani, S.Kom
Address	Jln. Jenderal Sudirman
City	Jambi
Region	Jambi
Country	Indonesia
Phone	0741-35095
Fax	0741-35093
Administrator E-mail	elibrarystikom@gmail.com
CKO E-mail	elibrarystikom@gmail.com