Abstract: A non-invasive diagnosis technique place an important role for early detection of melanoma malignant for dermoscopic image. Even for experienced dermatologist, diagnosis by human vision can be non-reproducible, inaccurate and subjective. The image characteristics include fuzzy lesion boundaries, varying lesion shapes and their sizes, presence of hair and color types of different skin. To assist in the interpretation of the image, the automatic classification of dermatoscopy images has proven to be a valuable aid in clinical decision making. However, existing methods have problems in representing and differentiating skin lesions due to the high degree of similarity between melanoma and non-melanoma images and large except from the images a variety of skin lesion. To overcome these limitations, this study proposes a new method for automatic multi-scale public of melanoma in dermoscopy images of the lesion is biased representation (1000) and vice versa general classification (JRC). For the diseases of the skin by means of the representation of the 1000 round, so that is set before us represent a number of scales, to use the traditional methods from a variety of Histograms of one of the scale is very near and the skin is able to represent the histogram of the lesion. 1000 or representation was used by the JRC melanoma detection. JRC The proposed model allows us to use additional data to derive a set of approximately Histograms of melanoma is going, where existing histogram like most trust in him. General Calendar method in public in our dataset of dermoscopy images, and demonstrates superior performance compared to the current state of the art method.
Keywords: JRC, Lesion Segmentation, MATLAB, Image Acquisition system.
|
DOI:
10.17148/IJARCCE.2021.107101
