Abstract
Skin diseases have a serious impact on people's life and health. Current research proposes an efficient approach to identify singular type of skin diseases. It is necessary to develop automatic methods in order to increase the accuracy of diagnosis for multitype skin diseases. In this paper, three type skin diseases such as herpes, dermatitis, and psoriasis skin disease could be identified by a new recognition method. Initially, skin images were preprocessed to remove noise and irrelevant background by filtering and transformation. Then the method of grey-level co-occurrence matrix (GLCM) was introduced to segment images of skin disease. The texture and color features of different skin disease images could be obtained accurately. Finally, by using the support vector machine (SVM) classification method, three types of skin diseases were identified. The experimental results demonstrate the effectiveness and feasibility of the proposed method.
Highlights
Composed of epidermis, dermis, and subcutaneous tissues, skin is the largest organ of human body, containing blood vessels, lymphatic vessels, nerves, and muscles, which can perspire, perceive the external temperature, and protect the body
The proposed methods mainly aim at the identification of one type of skin disease, which makes them difficult to apply to the precise identification of multitype skins. erefore, in this paper, a method based on vertical image segmentation, GLCM, and support vector machine (SVM) is proposed to identify three various types of skin diseases, namely, herpes, paederus dermatitis, and psoriasis
The epithelium can be divided into ten vertical image regions
Summary
Dermis, and subcutaneous tissues, skin is the largest organ of human body, containing blood vessels, lymphatic vessels, nerves, and muscles, which can perspire, perceive the external temperature, and protect the body. Erefore, in this paper, a method based on vertical image segmentation, GLCM, and SVM is proposed to identify three various types of skin diseases, namely, herpes, paederus dermatitis, and psoriasis. Three types of skin diseases’ features are extracted, and their correlated parameters of feature texture and pixels of lesion areas are collected through image segmentation. Considering that noise constantly exerts a negative impact on acquired samples of skin epidermis’s source images, it is necessary to denoise through median filtering for the reduction of the impact on skin segmentation and identification brought by irrelevant background in the images. E denoised skin epidermis’s source images are processed via neighborhood sampling with the intention of better obtaining the highlight line through the transformation of Euclidean distance.
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