A Method for Melanoma Skin Cancer Detection Using Dermoscopy Images

Mane, Soniya; Shinde, Swati

doi:10.1109/iccubea.2018.8697804

Cited by 42 publications

(16 citation statements)

References 6 publications

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“…Sensitivity, specificity, and accuracy are registered by utilizing an SVM classifier. It is seen that the SVM linear function gives [3] greater affectability and precision than the SVM RBF kernel. If there should arise an occurrence of explicitness SVM RBF kernel performs better compared to SVM linear function [3].…”

Section: • 5thmentioning

confidence: 98%

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Diagnosis of Skin Melanoma Cancer using Image Based Computer-Aided Diagnosis System from Dermoscopic Images

Zambare¹,

Patil²,

Chavan³

2021

IJRAT

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Skin malignant growth is the most widely recognized, everything being equal. Between 40 to 50 percent of all disease cases analyzed each year are skin malignant growth. Melanomas represent just four percent of all skin malignant growth cases yet are undeniably more perilous. Of all skin disease-related passing’s, 79 percent are from melanoma. Skin disease can be relieved if distinguished early. To appropriately distinguish melanoma, there is a need for a skin test. This is an obtrusive method and is the reason there is a requirement of a conclusion framework that can annihilate the skin test strategy emerges. We proposed to build up a Computer-Aided System that is equipped for ordering a skin injury as threatening or favorable by utilizing the ABCD rule which represents Asymmetry, Border, Color, Diameter of the skin sore. Further, the preprocessed pictures are portioned and commotions are taken out from the Dermoscopic pictures for instance hair and air bubbles. Also, finally, by utilizing a classifier, the proposed system identifies the pictures as favorable or harmful.

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Section: • 5thmentioning

confidence: 98%

“…In feature extraction, all 10 features are extricated in which edge, zone, anomaly, contrast, relationship, energy, homogeneity, and shading are the features. These features are given to the SVM classifier [3]. Results are gotten utilizing shading, shape, and surface highlights.…”

Section: • 5thmentioning

confidence: 99%

Diagnosis of Skin Melanoma Cancer using Image Based Computer-Aided Diagnosis System from Dermoscopic Images

Zambare¹,

Patil²,

Chavan³

2021

IJRAT

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“…Otsu thresholding+ ABCD rule + SVM (Mane, 2018) 90.47% Less time consuming and less costly. Dataset is not specified.…”

Section: 72%mentioning

confidence: 99%

A Survey on a Skin Disease Detection System

Mamun

Uddin

2021

International Journal of Healthcare Information Systems and Informatics

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Skin diseases are frequent and quite perennial in the world, and in some cases, these lead to cancer. These are curable if detected earlier and treated appropriately. An automated image-based detection system consisting of four main modules: image enhancement, region of interest segmentation, feature extraction, and detection can facilitate early identification of these diseases. Diverse image-based methods incorporating machine learning techniques are developed to diagnose different types of skin diseases. This article focuses on the review of the tools and techniques used in the diagnosis of 28 common skin diseases. Furthermore, it has discussed the available image databases and the evaluation metrics for the performance analysis of various diagnosis systems. This is vital for figuring out the implementation framework as well as the efficacy of the diagnosis methods for the neophyte. Based on the performance accuracy, the state-of-the-art method for the diagnosis of a particular disease is figured out. It also highlights challenges and shows future research directions.

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“…The clinical diagnosis of skin lesions rely on four features namely asymmetry, border, colour and diameter [3].The conventional steps for diagnosing skin cancer from dermoscopy images using image processing comprises of pre-processing, segmentation, feature extraction, and classification [1]. The skin image collected from the image acquisition device (source) mostly contain noise artifacts due to non-uniform illumination of light and in order to remove those conditions, a handful pre-processing steps are mandatory [2,4]. In many earlier works, the noise filtering was done by median filter [5,6].…”

Section: Introductionmentioning

confidence: 99%

Benign and Malignant Dermatoscopy Image Classification

Bobby

2020

2020 Third International Conference on Advances in Electronics, Computers and Communications (ICAECC)

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Skin cancer is one of the life threatening diseases and there is mostly no chance of remission from skin cancer if diagnosed in the last stage. The three major types of skin cancers are basal cell carcinoma, squamous cell carcinoma, and melanoma. Among these three, melanoma skin cancer is dangerous and acute in nature. Dermatologists use various techniques for diagnosing the malignant, in which the popular and reliable clinical method is dermatoscopy. The manual inference of the disease condition from the dermatoscopy images requires intensive knowledge and experience in the related field. Also, there will be an unavoidable degree of variability in image analysis occurs as long as the diagnostics procedure relies on human visual perception. Thus recently, image processing and machine learning algorithms have been applied for the accurate diagnoses of skin cancers from the dermatoscopic images. Thus, the goal of the proposed work is to automatically segment and classify the dermatoscopy skin lesion image with the help of image processing and machine learning algorithms. The proposed approach classifies the skin lesion image as benign or malignant melanoma with 90% accuracy, 91% sensitivity, 86% specificity, and 93% precision.

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A Method for Melanoma Skin Cancer Detection Using Dermoscopy Images

Cited by 42 publications

References 6 publications

Diagnosis of Skin Melanoma Cancer using Image Based Computer-Aided Diagnosis System from Dermoscopic Images

Diagnosis of Skin Melanoma Cancer using Image Based Computer-Aided Diagnosis System from Dermoscopic Images

A Survey on a Skin Disease Detection System

Benign and Malignant Dermatoscopy Image Classification

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