Computer Aided System for Diagnosis of Skin Cancer Using Classification (original) (raw)
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Detection of skin cancer " Melanoma " through Computer Vision
— In the last decades, skin cancer increased its incidence becoming a public health problem. Technological advances have allowed the development of applications that help the early detection of melanoma. In this context, an image processing was developed to obtain Asymmetry, Border, Color, and Diameter (ABCD of melanoma). Using neural networks to perform a classification of the different kinds of moles. As a result, this algorithm developed after an analysis of 200 images was obtained a performance of 97.51%.
Skin Cancer Detection using Digital Image Processing and Implementation using ANN and ABCD Features
International Journal of Trend in Scientific Research and Development, 2018
Melanoma is a serious type of skin cancer. It starts in skin cells called melanocytes. There are 3 main types of skin cancer, Melanoma, Basal and Squamous cell carcinoma. Melanoma is more likely to spread to other parts of the body. Early detection of malignant melanoma in dermoscopy images is very important and critical, since its detection in the early stage can be helpful to cure it. Computer Aided Diagnosis systems can be very helpful to facilitate the early detection of cancers for dermatologists. Image processing is a commonly used method for skin cancer detection from the appearance of affected area on the skin. In this work, a computerised method has been developed to make use of Neural Networks in the field of medical image processing. The ultimate aim of this paper is to implement cost-effective emergency support systems to process the medical images. It is more advantageous to patients. The dermoscopy image of suspect area of skin cancer is taken and it goes under various...
Neural Network Based Detection of Melanoma Skin Cancer
2013
Skin cancer is the deadliest form of cancers in humans. It is found in various types such as Melanoma, Basal and Squamous cell Carcinoma among which Melanoma is the most unpredictable. Detection of melanoma skin cancer in the earlier stage is very critical. In this paper, we explain the method for the detection of Melanoma Skin Cancer using Segmentation. The input to the system is the Dermoscopic Image and then apply and then by applying novel image processing techniques. The different stages of detection involves-collection of Dermoscopic Images, filtering the images by using Dull Razor filtering for removing hairs and air bubbles in the image, converting to gray scale, contrast enhancement, noise filtering, segmenting the images using Maximum Entropy Threshold. Feature Extraction technique used is Gray Level Co-occurrence Matrix (GLCM).It is a powerful tool for image feature extraction by mapping the gray level co-occurrence probabilities based on spatial relations of pixels in di...
— Skin cancer is the deadliest form of cancers in humans. Skin cancer is commonly known as Melanoma. Melanoma is named after the cell from which it presumably arises, the melanocyte. Skin Cancers are of two types-Benign and Malignant Melanoma. Melanoma can be cured completely if it is detected early. Both benign and malignant melanoma resembles similar in appearance at the initial stages. So it is difficult to differentiate both. This is a main problem with the early skin cancer detection. Only an expert dermatologist can classify which one is benign and which one is malignant. The Artificial Neural Network based Classification methodology uses Image processing techniques and Artificial Intelligence for early diagnosis. Main advantage of this computer based classification is that patient does not need to go to hospitals and undergo various painful diagnosing techniques like Biopsy. In this Computer Aided Classification, dermoscopy image of skin cancer is taken and it is subjected to various pre-processing and image enhancement. The cancer affected region is separated from the healthy skin using Segmentation. In order to reduce the complexity of classification, some unique features of malignant and benign melanoma are extracted. 2DWavelet transform is the Feature Extraction Method used. These features are given as the input to the Artificial Neural Network Classifier. It classifies the given data set into cancerous or non-cancerous.
An artificial neural network approach for detecting skin cancer
TELKOMNIKA Telecommunication Computing Electronics and Control, 2019
This study aims to present diagnose of melanoma skin cancer at an early stage. It applies feature extraction method of the first order for feature extraction based on texture in order to get high degree of accuracy with method of classification using artificial neural network (ANN). The method used is training and testing phases with classification of Multilayer Perceptron (MLP) neural network. The results showed that the accuracy of test image with 4 sets of training for image not suspected of melanoma and melanoma with the lowest accuracy of 80% and the highest accuracy of 88.88%, respectively. The 4 sets of training used consisted of 23 images. Of the 23 images used as a training consisted of 6 as not suspected of melanoma images and 17 as suspected melanoma images.
Computer-aided Diagnosis of Skin Cancer: A Review
Current Medical Imaging Formerly Current Medical Imaging Reviews
Cancer is currently one of the main health issues in the world. Among different varieties of cancers, skin cancer is the most common cancer in the world and accounts for 75% of the world's cancer. Indeed, skin cancer involves abnormal changes in the outer layer of the skin. Although most people with skin cancer recover, it is one of the major concerns of people due to its high prevalence. Most types of skin cancers grow only locally and invade adjacent tissues, but some of them, especially melanoma (cancer of the pigment cells), which is the rarest type of skin cancer, may spread through the circulatory system or lymphatic system and reach the farthest points of the body. Many papers have been reviewed about the application of image processing in cancer detection. In this paper, the automatic skin cancer detection and also different steps of such a process have been discussed based on the implantation capabilities.