File(s) not publicly available
Achievements of neural network in skin lesions classification
chapter
posted on 2023-07-26, 15:45 authored by Nazia Hameed, Antesar Shabut, Fozia Hameed, Silvia Cirstea, Alamgir HossainThe gross mismatch of skin disease cases and the specialties to manage them is the main cause of a continuously increased disease burden. The skin disease burden contributes 1.79% toward the global disease burden. To lessen this burden, automated skin lesions classification schemes that can provide multiclass classification are highly demanded. This chapter presents an investigation into an automated classification scheme to classify multiple skin lesions (acne, eczema, psoriasis; benign, and malignant) using state-of-the-art machine learning techniques. In the proposed classification scheme, convolution neural network (CNN) is utilized using the transfer learning approach, and a pretrained CNN model “AlexNet” is used to retrain the classification model on the skin lesion dataset. The proposed classification scheme outperformed over existing classification schemes and obtained an accuracy of 96.65%. The multiclass classification scheme can be very beneficial in the limited resource areas as it can assist in the early diagnosis of multiple skin lesions.
History
Refereed
- Yes
Volume
1Page range
133-151Number of pages
324Publisher
Academic PressPlace of publication
London, UKTitle of book
State of the Art in Neural Networks and their ApplicationsISBN
978-0-12-819740-0Editors
Ayman S. El-Baz, Jasjit S. SuriLanguage
- other
Legacy posted date
2022-04-04Legacy Faculty/School/Department
Faculty of Science & EngineeringUsage metrics
Categories
No categories selectedKeywords
Licence
Exports
RefWorks
BibTeX
Ref. manager
Endnote
DataCite
NLM
DC