Early Detection of Melanoma Skin Cancer

Alendar, Faruk; Drljević, Irdina; Drljević, Kenan; Alendar, Temeida

doi:10.17305/bjbms.2009.2861

Cited by 13 publications

(5 citation statements)

References 3 publications

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“…Skin cancer is a major public health issue [4]. The major etiological factor associated with skin cancer, both non-melanoma (basal and squamous cell) and melanoma, is exposure to ultraviolet (UV) radiation [5].…”

mentioning

confidence: 99%

Adolescent Attitudes Towards Tanning: Does Age Matter?

2009

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Adolescents seem to be obsessed with tanning. One third of both adolescents and adults report sunbathing. On average, about 20% of adolescents report ever-using of artificial UV tanning devices with 15% currently using them. Tanning attitudes appear similar for adolescents and adults. The top three reasons for tanning included beliefs that a tan makes them more comfortable socializing with friends, looks better and is attractive. The top reason for using artificial UV tanning devices is also aesthetics, followed by relaxation. Other reasons for using artificial UV tanning devices include vacation preparation and to gain a protective base. Attitudes related to appearance that result in more frequent tanning may be difficult to change.

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mentioning

confidence: 99%

Adolescent Attitudes Towards Tanning: Does Age Matter?

2009

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“…Recent studies have shown that there are approximately three commonly known types of skin cancers. These include Melanoma, Kaposi's sarcoma and Actinic Keratosis [1]. However, melanoma has been considered to be one of the most hazardous types in the sense that it is deadly and its prevalence has enormously increased with time.…”

Section: A Background and Motivationmentioning

confidence: 99%

“…A skin that has inadequate melanin is exposed to the risk of sunburn as well as harmful ultra-violet rays from the sun [3]. This disorder has been proven to be unpredictable [1]. It is characterized by development of lesions in the skin that vary in shape, size, color and texture.…”

Section: A Background and Motivationmentioning

confidence: 99%

A comparison of feature sets for an automated skin lesion analysis system for melanoma early detection and prevention

Abuzaghleh¹,

Faezipour²,

Barkana³

2015

2015 Long Island Systems, Applications and Technology

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One in five Americans will develop skin cancer in their lifetime, and on average, one American dies from skin cancer every hour. Melanoma spreads through metastasis, and can often be fatal. Statistical evidence has revealed that the majority of deaths resulting from skin cancer are as a result of melanoma. Further investigations have shown that the survival rates in patients depend on the stage of the cancer at the time it is first diagnosed; early detection and intervention of melanoma indicates higher chances of cure. Clinical diagnosis and prognosis of melanoma are challenging since the processes are prone to misdiagnosis and inaccuracies due to doctors' subjectivity. Malignant melanomas are asymmetrical, have irregular borders, notched edges, and color variations, so analyzing the shape, color, and texture of the skin lesion is important for melanoma early detection and prevention. This paper proposes the two major components of a noninvasive real-time automated skin lesion analysis system for melanoma early detection and prevention. The first component is a real-time alert to help users prevent skin burn caused by sunlight; a novel equation to compute the time for skin to burn is thereby introduced. The second component is an automated image analysis module which contains image acquisition, hair detection and exclusion, lesion segmentation, feature extraction, and classification. The proposed system uses PH2 Dermoscopy image database from Pedro Hispano Hospital for development and testing purposes. The image database contains a total of 200 dermoscopy images of lesions, including benign, atypical, and melanoma cases. A comparison of the performance of all feature sets is presented in this paper in order to determine what feature sets provide the best classification results.Index Terms-image segmentation, skin cancer, melanoma.

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“…Skin cancers have been on the rise in recent decades, becoming a very significant public health issue [1]. Among the different types of skin cancer, melanoma is one of the deadliest types, including 80% of deaths from skin cancer [2].…”

Section: Introductionmentioning

confidence: 99%

Automatic Skin Cancer Detection Using Clinical Images: A Comprehensive Review

Nazari,

Garcia

2023

Life

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Skin cancer has become increasingly common over the past decade, with melanoma being the most aggressive type. Hence, early detection of skin cancer and melanoma is essential in dermatology. Computational methods can be a valuable tool for assisting dermatologists in identifying skin cancer. Most research in machine learning for skin cancer detection has focused on dermoscopy images due to the existence of larger image datasets. However, general practitioners typically do not have access to a dermoscope and must rely on naked-eye examinations or standard clinical images. By using standard, off-the-shelf cameras to detect high-risk moles, machine learning has also proven to be an effective tool. The objective of this paper is to provide a comprehensive review of image-processing techniques for skin cancer detection using clinical images. In this study, we evaluate 51 state-of-the-art articles that have used machine learning methods to detect skin cancer over the past decade, focusing on clinical datasets. Even though several studies have been conducted in this field, there are still few publicly available clinical datasets with sufficient data that can be used as a benchmark, especially when compared to the existing dermoscopy databases. In addition, we observed that the available artifact removal approaches are not quite adequate in some cases and may also have a negative impact on the models. Moreover, the majority of the reviewed articles are working with single-lesion images and do not consider typical mole patterns and temporal changes in the lesions of each patient.

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Early Detection of Melanoma Skin Cancer

Cited by 13 publications

References 3 publications

Adolescent Attitudes Towards Tanning: Does Age Matter?

Adolescent Attitudes Towards Tanning: Does Age Matter?

A comparison of feature sets for an automated skin lesion analysis system for melanoma early detection and prevention

Automatic Skin Cancer Detection Using Clinical Images: A Comprehensive Review

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