{"title":"基于不同分类器的非下采样弯曲变换检测黑色素瘤的若干研究","authors":"S. Poovizhi, T. R. Ganesh Babu, R. Praveena","doi":"10.32604/mcb.2021.017984","DOIUrl":null,"url":null,"abstract":"Skin is the largest organ and outer enclosure of the integumentary system that protects the human body from pathogens. Among various cancers in the world, skin cancer is one of the most commonly diagnosed cancer which can be either melanoma or non-melanoma. Melanoma cancers are very fatal compared with non-melanoma cancers but the chances of survival rate are high when diagnosed and treated earlier. The main aim of this work is to analyze and investigate the performance of Non-Subsampled Bendlet Transform (NSBT) on various classifiers for detecting melanoma from dermoscopic images. NSBT is a multiscale and multidirectional transform based on second order shearlet system which precisely classifies the curvature over other directional representation systems. Here two-phase classification is employed using k-Nearest Neighbour (kNN), Naive Bayes (NB), Decision Trees (DT) and Support Vector Machines (SVM). The first phase classification is used to classify the images of PH2 database into normal and abnormal images and the second phase classification classifies the abnormal images into benign and malignant. Experimental result shows the improvement in classification accuracy, sensitivity and specificity compared with the state of art methods.","PeriodicalId":48719,"journal":{"name":"Molecular & Cellular Biomechanics","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Certain Investigations on Melanoma Detection Using Non-Subsampled Bendlet Transform with Different Classifiers\",\"authors\":\"S. Poovizhi, T. R. Ganesh Babu, R. Praveena\",\"doi\":\"10.32604/mcb.2021.017984\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Skin is the largest organ and outer enclosure of the integumentary system that protects the human body from pathogens. Among various cancers in the world, skin cancer is one of the most commonly diagnosed cancer which can be either melanoma or non-melanoma. Melanoma cancers are very fatal compared with non-melanoma cancers but the chances of survival rate are high when diagnosed and treated earlier. The main aim of this work is to analyze and investigate the performance of Non-Subsampled Bendlet Transform (NSBT) on various classifiers for detecting melanoma from dermoscopic images. NSBT is a multiscale and multidirectional transform based on second order shearlet system which precisely classifies the curvature over other directional representation systems. Here two-phase classification is employed using k-Nearest Neighbour (kNN), Naive Bayes (NB), Decision Trees (DT) and Support Vector Machines (SVM). The first phase classification is used to classify the images of PH2 database into normal and abnormal images and the second phase classification classifies the abnormal images into benign and malignant. Experimental result shows the improvement in classification accuracy, sensitivity and specificity compared with the state of art methods.\",\"PeriodicalId\":48719,\"journal\":{\"name\":\"Molecular & Cellular Biomechanics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Molecular & Cellular Biomechanics\",\"FirstCategoryId\":\"1087\",\"ListUrlMain\":\"https://doi.org/10.32604/mcb.2021.017984\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"Biochemistry, Genetics and Molecular Biology\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Molecular & Cellular Biomechanics","FirstCategoryId":"1087","ListUrlMain":"https://doi.org/10.32604/mcb.2021.017984","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Biochemistry, Genetics and Molecular Biology","Score":null,"Total":0}
Certain Investigations on Melanoma Detection Using Non-Subsampled Bendlet Transform with Different Classifiers
Skin is the largest organ and outer enclosure of the integumentary system that protects the human body from pathogens. Among various cancers in the world, skin cancer is one of the most commonly diagnosed cancer which can be either melanoma or non-melanoma. Melanoma cancers are very fatal compared with non-melanoma cancers but the chances of survival rate are high when diagnosed and treated earlier. The main aim of this work is to analyze and investigate the performance of Non-Subsampled Bendlet Transform (NSBT) on various classifiers for detecting melanoma from dermoscopic images. NSBT is a multiscale and multidirectional transform based on second order shearlet system which precisely classifies the curvature over other directional representation systems. Here two-phase classification is employed using k-Nearest Neighbour (kNN), Naive Bayes (NB), Decision Trees (DT) and Support Vector Machines (SVM). The first phase classification is used to classify the images of PH2 database into normal and abnormal images and the second phase classification classifies the abnormal images into benign and malignant. Experimental result shows the improvement in classification accuracy, sensitivity and specificity compared with the state of art methods.
期刊介绍:
The field of biomechanics concerns with motion, deformation, and forces in biological systems. With the explosive progress in molecular biology, genomic engineering, bioimaging, and nanotechnology, there will be an ever-increasing generation of knowledge and information concerning the mechanobiology of genes, proteins, cells, tissues, and organs. Such information will bring new diagnostic tools, new therapeutic approaches, and new knowledge on ourselves and our interactions with our environment. It becomes apparent that biomechanics focusing on molecules, cells as well as tissues and organs is an important aspect of modern biomedical sciences. The aims of this journal are to facilitate the studies of the mechanics of biomolecules (including proteins, genes, cytoskeletons, etc.), cells (and their interactions with extracellular matrix), tissues and organs, the development of relevant advanced mathematical methods, and the discovery of biological secrets. As science concerns only with relative truth, we seek ideas that are state-of-the-art, which may be controversial, but stimulate and promote new ideas, new techniques, and new applications.