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.
Highlights
Skin is the most sensitive part that is more affected than any other human organ
For the segmentation and feature extraction process, an enhanced image of the affected portion of skin is used, and extracted features are used in the machine learning or artificial neural network algorithm to identify whether there exist skin diseases or not
The skin disease detection system plays a vital role in identifying these diseases accurately at the initial stage
Summary
Skin is the most sensitive part that is more affected than any other human organ. Sunburn is one of the essential elements that affect the melanocytes cell due to the ultraviolet (UV) rays from the sun [1]. For the segmentation and feature extraction process, an enhanced image of the affected portion of skin is used, and extracted features are used in the machine learning or artificial neural network algorithm to identify whether there exist skin diseases or not. Machine learning or ANN algorithms are used to identify skin diseases by extracting features [10] [11]. We have found efficient hybrid methods of segmentation, feature extraction, and classification of skin diseases. These automated hybrid methods are functioning effectively with low computational complexity. The existing state-of-the-art hybrid methods of three processes, such as segmentation, feature extraction, and classification for detecting skin diseases are summarized in part V. The last section concludes the study along with future research guidelines
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