Detection and diagnosis of melanoma skin cancers in dermoscopic images using pipelined internal module architecture (PIMA) method.
deep learning
diagnosis
features
melanoma
morphological
skin
Journal
Microscopy research and technique
ISSN: 1097-0029
Titre abrégé: Microsc Res Tech
Pays: United States
ID NLM: 9203012
Informations de publication
Date de publication:
Jun 2023
Jun 2023
Historique:
revised:
10
02
2023
received:
06
12
2022
accepted:
13
02
2023
medline:
17
5
2023
pubmed:
3
3
2023
entrez:
2
3
2023
Statut:
ppublish
Résumé
Detection and diagnosis of melanoma skin cancer is important to save the life of humans. The main objective of this article is to perform both detection and diagnosis of the skin cancers in dermoscopy images. Both skin cancer detection and diagnosis system uses deep learning architectures for the effective performance improvement as the main objective. The detection process involves by identifying the cancer affected skin dermoscopy images and the diagnosis process involves by estimating the severity levels of the segmented cancer regions in skin images. This article proposes parallel CNN architecture for the classification of skin images into either melanoma or healthy. Initially, color map histogram equalization (CMHE) method is proposed in this article to enhance the source skin images and then thick and thin edges are detected from the enhanced skin image using the Fuzzy system. The gray-level co-occurrence matrix (GLCM) and Law's texture features are extracted from the edge detected images and these features are optimized using genetic algorithm (GA) approach. Further, the optimized features are classified by the developed pipelined internal module architecture (PIMA) of deep learning structure. The cancer regions in the classified melanoma skin images are segmented using mathematical morphological process and these segmented cancer regions are diagnosed into either mild or severe using the proposed PIMA structure. The proposed PIMA-based skin cancer classification system is applied and tested on ISIC and HAM 10000 skin image datasets. RESEARCH HIGHLIGHTS: The melanoma skin cancer is detected and classified using dermoscopy images. The skin dermoscopy images are enhanced using color map histogram equalization. GLCM and Law's texture features are extracted from the enhanced skin images. To propose pipelined internal module architecture (PIMA) for the classification of skin images.
Substances chimiques
Potassium Iodide
1C4QK22F9J
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
701-713Informations de copyright
© 2023 Wiley Periodicals LLC.
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