Diagnosis of Melanoma Using Deep Learning

Malik Bader Alazzam, Fawaz Alassery and Ahmed Almulihi

Mathematical Problems in Engineering, 2021, vol. 2021, 1-9

Abstract:

When compared to other types of skin cancer, melanoma is the deadliest. However, those who are diagnosed early on have a better prognosis for the purpose of providing a supplementary opinion to experts; various methods of spontaneous melanoma recognition and diagnosis have been investigated by different researchers. Because of the imbalance between classes, building models from existing information has proven difficult. Machine learning algorithms paired with imbalanced basis training approaches are being evaluated for their performance on the melanoma diagnosis challenge in this study. There were 200 dermoscopic photos in which patterns of skin lesions could be extracted using the VGG16, VGG19, Inception, and ResNet convolutional neural network architectures with the ABCD rule. After employing attribute selection with GS and training data balance using Synthetic Minority Oversampling Technique and Edited Nearest Neighbor rule, the random forest classifier had a sensitivity of nearly 93% and a kappa index ( ) of 78%.

Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
http://downloads.hindawi.com/journals/MPE/2021/1423605.pdf (application/pdf)
http://downloads.hindawi.com/journals/MPE/2021/1423605.xml (text/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:1423605

DOI: 10.1155/2021/1423605

Access Statistics for this article

More articles in Mathematical Problems in Engineering from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().