Skin cancer

Lacy, Katie E.; Alwan, Wisam

doi:10.1016/j.mpmed.2013.04.008

Cited by 18 publications

(7 citation statements)

References 9 publications

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“…According to World Health Organization (WHO) (1992) and many other authors [69,70], exposure to UVR radiation is considered to be a major etiological factor for all three forms of melanoma (i) basal cell carcinoma (BCC), (ii) squamous cell carcinoma (SCC), and (iii) malignant melanoma (MM). We found the correlation between MM and climate changes impact on UVR and also the number of HD.…”

Section: Melanoma Incidence and Uvrmentioning

confidence: 99%

Assessment of climate change impact on the malaria vector Anopheles hyrcanus, West Nile disease, and incidence of melanoma in the Vojvodina Province (Serbia) using data from a regional climate model

Mihailoviĉ

Petrić

Hrnjakovic-Cvjetkovic

et al. 2020

PLoS ONE

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Motivated by the One Health paradigm, we found the expected changes in temperature and UV radiation (UVR) to be a common trigger for enhancing the risk that viruses, vectors, and diseases pose to human and animal health. We compared data from the mosquito field collections and medical studies with regional climate model projections to examine the impact of climate change on the spreading of one malaria vector, the circulation of West Nile virus (WNV), and the incidence of melanoma. We analysed data obtained from ten selected years of standardised mosquito vector sampling with 219 unique location-year combinations, and 10 years of melanoma incidence. Trends in the observed data were compared to the climatic variables obtained by the coupled regional Eta Belgrade University and Princeton Ocean Model for the period 1961-2015 using the A1B scenario, and the expected changes up to 2030 were presented. Spreading and relative abundance of Anopheles hyrcanus was positively correlated with the trend of the mean annual temperature. We anticipated a nearly twofold increase in the number of invaded sites up to 2030. The frequency of WNV detections in Culex pipiens was significantly correlated to overwintering temperature averages and seasonal relative humidity at the sampling sites. Regression model projects a twofold increase in the incidence of WNV positive Cx. pipiens for a rise of 0.5˚C in overwintering T October-April temperatures. The projected increase of 56% in the number of days with T max � 30˚C (Hot Days-HD) and UVR doses (up to 1.2%) corresponds to an increasing trend in melanoma incidence. Simulations of the Pannonian countries climate anticipate warmer and drier conditions with possible dominance of temperature and number of HD

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Section: Melanoma Incidence and Uvrmentioning

confidence: 99%

Assessment of climate change impact on the malaria vector Anopheles hyrcanus, West Nile disease, and incidence of melanoma in the Vojvodina Province (Serbia) using data from a regional climate model

Mihailoviĉ

Petrić

Hrnjakovic-Cvjetkovic

et al. 2020

PLoS ONE

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“…In the human body, one can find the integumentary system, usually known as skin, whose primary functions are excretory, protective, relationship, thermoregulatory and metabolic. However, in addition to other factors, when the skin is repeatedly exposed to the sun over long periods, there is a high possibility for occurring skin lesions (Lacy and Alwan, 2013).…”

Section: Introductionmentioning

confidence: 99%

Semi-automatic segmentation of skin lesions based on superpixels and hybrid texture information

Santos

Veras

Aires

et al. 2022

Medical Image Analysis

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Dermoscopic images are commonly used in the early diagnosis of skin lesions, and several computational systems have been proposed to analyze them. The segmentation of the lesions is a fundamental step in many of these systems. Therefore, a semi-automatic segmentation method is proposed here, which begins by building the superpixels of the image under analysis based on the zero parameter version of the simple linear iterative clustering (SLIC0) algorithm. Then, each superpixel is represented using a descriptor built by combining the grey-level co-occurrence matrix and Tamura texture features. Afterward, the gain ratios of the features are used to select the input for the semisupervised seeded fuzzy C-means clustering algorithm. Hence, from a few specialistselected superpixels, this clustering algorithm groups the built superpixels into lesion or background regions. Finally, the segmented image undergoes a post-processing step to eliminate sharp edges. The experiments were performed on 1,380 images: 401 images from the PH 2 and DermIS datasets, which were used to establish the parameters of the method, and 3,573 images from the ISIC 2016, ISIC 2017 and ISIC 2018 datasets were used for the analysis of the method's performance. The findings suggest that, by manually identifying just a few of the generated superpixels, the method can achieve an average segmentation accuracy of 96.78%, which confirms its superiority to the ones in the literature.

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“…Since the 1990s, the incidence rate for melanomas has increased over 128% with around 15,000 incidences every year and the mortality rate in 2016 being approximately 15% [3]. The most investigated and well-known risk factor for developing melanomas is exposure to ultraviolet or UV light [4].…”

Section: Introductionmentioning

confidence: 99%

Deep learning based melanoma diagnosis using dermoscopic images

Wall

Young

Zhang

et al. 2020

Developments of Artificial Intelligence Technologies in Computation and Robotics

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The most common malignancies in the world are skin cancers, with melanomas being the most lethal. The emergence of Convolutional Neural Networks (CNNs) has provided a highly compelling method for medical diagnosis. This research therefore conducts transfer learning with grid search based hyper-parameter fine-tuning using six state-ofthe-art CNN models for the classification of benign nevus and malignant melanomas, with the models then being exported, implemented, and tested on a proof-of-concept Android application. Evaluated using Dermofit Image Library and PH2 skin lesion data sets, the empirical results indicate that the ResNeXt50 model achieves the highest accuracy rate with fast execution time, and a relatively small model size. It compares favourably with other related methods for melanoma diagnosis reported in the literature.

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Skin cancer

Cited by 18 publications

References 9 publications

Assessment of climate change impact on the malaria vector Anopheles hyrcanus, West Nile disease, and incidence of melanoma in the Vojvodina Province (Serbia) using data from a regional climate model

Assessment of climate change impact on the malaria vector Anopheles hyrcanus, West Nile disease, and incidence of melanoma in the Vojvodina Province (Serbia) using data from a regional climate model

Semi-automatic segmentation of skin lesions based on superpixels and hybrid texture information

Deep learning based melanoma diagnosis using dermoscopic images

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