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Arasi, M.A., El-Horbaty, E.-S.M., Abdel-Badeeh, M. and El-Dahshan, E.-S.A. (2018) Classification of Dermoscopy Images Using Naïve Bayesian and Decision Tree Techniques. 1st International Conference on Information and Sciences (AICIS), Fallujah, 20-21 November 2018, Vol. 67, 7-12.
https://doi.org/10.1109/AiCIS.2018.00015

has been cited by the following article:

• TITLE: Hybrid Methodologies for Segmentation and Classification of Skin Diseases: A Study

  AUTHORS: Md. Al Mamun, Mohammad Shorif Uddin

  KEYWORDS: Segmentation, Feature Extraction, Classification, Machine Learning

  JOURNAL NAME: Journal of Computer and Communications, Vol.9 No.4, April 19, 2021

  ABSTRACT: Skin disorders are a serious global health problem for humans. These disorders become dangerous when they grow into the malignant stage. Hence, it is necessary to detect these diseases at their early stage. A mobile-based automated skin disease detection system is vital for detecting skin diseases. This system also offers cure or treatment plans to the affected person through the short message service (SMS) or electronic mail (e-mail). An effective skin disease detection system consists of three processes: segmentation, feature extraction, and classification. Several hybrid methodologies are already developed for the above-mentioned processes for detecting skin diseases at the initial stage. This research gives a standard hybrid framework for detecting skin diseases and highlights some design requirements for achieving high accuracy. Existing state-of-the-art hybrid methods of three processes for detecting skin diseases along with their limitations are also summarized. It also identifies the challenges for developing an effective skin disease detection system and gives future research directions.