Performance analysis of melanoma early detection using skin lession classification system

Sundar, R.; Vadivel, M.

doi:10.1109/iccpct.2016.7530182

Cited by 22 publications

(10 citation statements)

References 7 publications

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“…• Sundar et. Al [13], In this paper , Multiclass support vector machine (MSVM) is proposed as a novel scheme for early detection of melanoma. There are five different skin lesions which are assembled as Solar Keratosis or actinic keratosis, Basal Cell Cancer, Nevocytic nevus, Squamous Cell Cancer, Seborrhoeic Verruca.The proposed system uses an automatic procedure, where the inquiry images are grouped and matched with higher probability type to classify the type of melanoma.…”

Section: • Garg Et Al [9]mentioning

confidence: 99%

Machine Learning Techniques for the Skin Cancer Detection: A Review

2022

IRJMETS

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Skin cancer is the most common cancer worldwide, and occurrence rates are continuing to increase. The prediction of the skin cancer is essential to eliminate it on time, so that the life of patient may be saved. The early-stage detection of such disease is helpful for increasing the life span of suffering patient. In addition, it also reduces the cost of treatment. It is better to build an precise expert system, which helps to detect these diseases before leading to terrible conditions. Therefore, many lives can be saved by using these intuitive systems. This paper provides the concise review of medical expert systems and presents the best methods used to recognize the skin cancer.

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Section: • Garg Et Al [9]mentioning

confidence: 99%

Machine Learning Techniques for the Skin Cancer Detection: A Review

2022

IRJMETS

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“…MSVM performed slightly better than ELM. In [126], Sundar et al proposed a technique based on SVM and image processing that is capable of distinguishing among five types of skin melanomas. Author extracted features related to colour, gradient, texture, edges and contrast.…”

Section: Computational Complexitymentioning

confidence: 99%

A Review on Automated Cancer Detection in Medical Images using Machine Learning and Deep Learning based Computational Techniques: Challenges and Opportunities

Manhas

Gupta

Roy

2021

Arch Computat Methods Eng

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Cancer is one of the most deadly diseases diagnosed among the population across the globe so far. The number of cases is increasing at a high pace each year that subsequently leads to the advancement in different diagnosis tools and technologies to handle this pandemic. Significant increase in the mortality rate worldwide leads tremendous scope to device and implement latest computer aided diagnostic systems for its early detection. The one among such techniques is machine learning coupled with medical imaging modalities. This combination has proven to be efficient in diagnosing various medical conditions in cancer diagnosis. Current study presents a review of different machine learning techniques applied on imaging modalities for cancer diagnosis from 2008 to 2019. This study focuses on diagnosis of five most prevalent and deadly cancers i.e., cervical cancer, oral cancer, breast cancer, brain cancer and skin cancer. Extensive and exhaustive review was carried out after going through different research papers, research articles and book chapters published by reputed international and national publishers such as Springer Link, Science Direct, IEEE Xplore Digital library and PubMed. A number of conference proceedings have also been included subject to the fulfilling of our quality evaluation criteria. This review article provides a comprehensive overview of machine learning approaches using image modalities for cancer detection and diagnosis with main focus on challenges being faced during their research. Majority of the challenges are identified based on the use of potential machine learning based approaches, image modalities, features and evaluation metrics. This review not only identified challenges but also ear mark and present the new research opportunities for researchers working in this field. It has been widely observed that traditional machine learning algorithms Like SVM, GMM performed excellent in classification whereas the deep learning has dominated the field of medical image analysis to a greater extent. It is evident from the literature survey that the researchers have achieved the accuracies of 100% in classification of cancerous and normal tissue images using different machine learning techniques. This article will provide an insight to the researchers working in this domain to identify which machine learning technique work best on what type of data set, selection of features, various challenges and their proposed solutions in solving this complex problem. Limitations and future research opportunities in the field of implementing different machine learning techniques in cancer diagnosis and classification is also presented at the end of this review article.

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“…An early melanoma classification system is discussed in [5] using different color space. GLCM features like contrast, sum variance, average, autocorrelation, sum variance and average are extracted.…”

Section: Introductionmentioning

confidence: 99%

Skin Lesion Classification System Using Shearlets

Kumar¹,

Kumanan²

2023

Computer Systems Science and Engineering

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The main cause of skin cancer is the ultraviolet radiation of the sun. It spreads quickly to other body parts. Thus, early diagnosis is required to decrease the mortality rate due to skin cancer. In this study, an automatic system for Skin Lesion Classification (SLC) using Non-Subsampled Shearlet Transform (NSST) based energy features and Support Vector Machine (SVM) classifier is proposed. At first, the NSST is used for the decomposition of input skin lesion images with different directions like 2, 4, 8 and 16. From the NSST's sub-bands, energy features are extracted and stored in the feature database for training. SVM classifier is used for the classification of skin lesion images. The dermoscopic skin images are obtained from PH 2 database which comprises of 200 dermoscopic color images with melanocytic lesions. The performances of the SLC system are evaluated using the confusion matrix and Receiver Operating Characteristic (ROC) curves. The SLC system achieves 96% classification accuracy using NSST's energy features obtained from 3 rd level with 8-directions.

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Performance analysis of melanoma early detection using skin lession classification system

Cited by 22 publications

References 7 publications

Machine Learning Techniques for the Skin Cancer Detection: A Review

Machine Learning Techniques for the Skin Cancer Detection: A Review

A Review on Automated Cancer Detection in Medical Images using Machine Learning and Deep Learning based Computational Techniques: Challenges and Opportunities

Skin Lesion Classification System Using Shearlets

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