Skin cancer is a type of disease that grows around human skin. There are two main types of skin cancer, non-melanoma and melanoma where non-melanoma skin cancer is more frequent in patients than melanoma.
Based on the statement issued by the World Health Organization WHO , an estimated , cancer melanoma occur globally each year and approximately 66, deaths were reported. The existing system for detecting skin cancers only uses image inputs that have many blurring and noise disruption. It is because the equipment cannot produce a good resolution at a less appropriate atmosphere. This will reduce the accuracy of the segmentation process and also affect the second process, classification.
There are also some research projects conducted related to an intelligent decision support system for skin lesion recognition to help early diagnosis by using an intelligent agent-based system. Agent technology is a new paradigm suitable for developing such systems that situates and operates in a dynamic and heterogeneous environment Chin et al. Image segmentation process, the process of separating skin cancer moles with healthy skin found in skin cancer images is used in developing this type of melanoma skin cancer detection system.
Among the image segmentation techniques previously used by the previous researcher are the technique of mining which is a technique based on the pixel equation in the image Lawand, In addition, the image grading technique based on gradient Mahmoud, , a k-min Hamzah et al. Transactions on Science and Technology. The first process is the image segmentation and the second process is the extraction of melanoma skin cancer features as well as classification based on the extracted features.
Regional based segmentation is one of the common techniques used to separate or group images into small sections based on the common features of the image Castillejos et al. Read the colour images RGB and replace the colour space of the image to the grayscale.
Using magnitude gradients as a segmentation function. Generate markers for image backgrounds. Use the segmentation function for watershed transformation. Use the Canny edge detection technique to draw the boundary between the foreground and the background. Feature Extraction and Classification Four features of melanoma skin cancer are extracted in the image of skin cancer using the ABCD rule: i. Asymmetry; in which the moles found on the image of skin cancer is not equal when halved.
Border; the irregular border of the moles. Diameter; the mole diameter when it exceeds 6mm. This is the image pre-processing step to get the fixed shape of the mole before the colour feature extraction can be made. The determination is based on the calculated TDS value by referring to the interpretation by Stolz et al. Table 1. Classification of skin cancer image based on TDS. Table 2 is an example of detectable and undetectable image of melanoma and non-melanoma skin cancer images.
Table 2. Output classification of melanoma and non-melanoma skin images. As shown in Table 2 for Melanoma input image, asymmetrical feature in second image is undetectable, because when the ROI on the image is equally split by two, both parts are almost symmetry.
Another factor is the image of non-melanoma skin cancer that has one or more properties possessed by melanoma ISSN Additionally, segmentation comparison tests suggest that the proposed marker-controlled watershed and Canny edge detection techniques have some advantages over other segmentation techniques. This segmentation technique can distinguish ROI as well as less important objects in skin cancer images. Transactions on Science and Technology, 4 4 , — Computational and Mathematical Methods in Medicine, , 41— We develop a deep learning algorithm for contour detection with a fully convolutional encoder-decoder network.
Edge detection is among the most fundamental vision problems for its role in perceptual grouping and its wide applications.
We find that reliance, solely, on visual assessment can be misleading. This dataset has been used for training the proposed approach as well the state-of-the-art algorithms for comparisons. A fundamental question for edge detection in noisy images is how faint can an edge be and still be detected. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets.
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