Prevention of Occupational Skin Cancer

Bauer, Andrea; Adam, Kerry; Soyer, Peter; Adam, Keith William James

doi:10.1007/978-3-319-68617-2_115

Cited by 7 publications

(4 citation statements)

References 52 publications

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“…People having fair skin are prone to skin cancer. The two types of skin cancer are malignant and benign [ 1 ]. Cancerous cells that grow without any control leads to malignant tumors.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Transfer Learning Based Classification for Diagnosis of Skin Cancer

et al. 2022

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Skin cancer is the most commonly diagnosed and reported malignancy worldwide. To reduce the death rate from cancer, it is essential to diagnose skin cancer at a benign stage as soon as possible. To save lives, an automated system that can detect skin cancer in its earliest stages is necessary. For the diagnosis of skin cancer, various researchers have performed tasks using deep learning and transfer learning models. However, the existing literature is limited in terms of its accuracy and its troublesome and time-consuming process. As a result, it is critical to design an automatic system that can deliver a fast judgment and considerably reduce mistakes in diagnosis. In this work, a deep learning-based model has been designed for the identification of skin cancer at benign and malignant stages using the concept of transfer learning approach. For this, a pre-trained VGG16 model is improved by adding one flatten layer, two dense layers with activation function (LeakyReLU) and another dense layer with activation function (sigmoid) to enhance the accuracy of this model. This proposed model is evaluated on a dataset obtained from Kaggle. The techniques of data augmentation are applied in order to enhance the random-ness among the input dataset for model stability. The proposed model has been validated by considering several useful hyper parameters such as different batch sizes of 8, 16, 32, 64, and 128; different epochs and optimizers. The proposed model is working best with an overall accuracy of 89.09% on 128 batch size with the Adam optimizer and 10 epochs and outperforms state-of-the-art techniques. This model will help dermatologists in the early diagnosis of skin cancers.

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“…People having fair skin are prone to skin cancer. The two types of skin cancer are malignant and benign [ 1 ]. Cancerous cells that grow without any control leads to malignant tumors.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Transfer Learning Based Classification for Diagnosis of Skin Cancer

et al. 2022

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“…This section has reviewed the selected skin cancerrelated articles where cloud computing played an important role. Skin cancer, a dermatological illness, appears due to the occupational ultraviolet irradiation affecting a substantial number of employees, particularly in agriculture and construction [131]. Skin cancer is a prevalent type of cancer worldwide, and its frequency is growing.…”

Section: E Skin Cancermentioning

confidence: 99%

The Roles of Cloud-Based Systems on the Cancer-Related Studies: A Systematic Literature Review

Zhou

2022

IEEE Access

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The advances in Wireless-based technologies and intelligent diagnostics and forecasting such as cloud computing have significantly affected our lifestyle, observed in many fields, especially healthcare. Also, since the number of new cases of cancer has become very high, there is a need to investigate this matter deeply. Still, there is no systematic review on the application or implementation of the cloud in cancer-care services. Hence, this paper has introduced a comprehensive review of a cloud-centered healthcare system that emphasizes treatment ways in different types of cancer until Sep 2021. The results have shown that the largest study was about the relationship between cancer and the cloud associated with breast cancer. Also, the results have shown that cloud computing facilitates data protection, privacy, and medical record access. Using cloud computing in hospitals, physicians will use advanced programs and tools, and nurses will quickly access patients' information with new Wireless-based technologies. A strong understanding of the practical aspects of cloud computing will help researchers effectively navigate the vast data ecosystems in cancer research. So, by highlighting the advantages and drawbacks of analyzed articles, this study provides a comprehensive and up-to-date report on the field of cloud-based cancer studies to fill the previous gaps.

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“…Solar UVR is the most pertinent occupational carcinogenic exposure [4][5][6]. Construction workers, gardeners, fishermen/women, and farmers are examples of jobs with a high frequency of outdoor work and direct occupational solar UVR exposure (i.e., spending the majority of their working hours outside and being thus directly exposed to high levels of UVR); aircraft maintenance engineers, building and construction managers, childcare workers, and police officers are examples of outdoor workers with rather indirect, but potentially significant occupational solar UVR exposure [7,8]. Epidemiologic data reveal a high prevalence of both BCC and SCC among outdoor workers with several years of cumulative sunlight exposure, demonstrating a strong link between cumulative occupational UVR exposure and the incidence of NMSC [9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Risk of cutaneous squamous cell carcinoma due to occupational exposure to solar ultraviolet radiation: Protocol for a systematic review and meta-analysis

Paulo

Symanzik²,

Ádám³

et al. 2023

PLoS ONE

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Solar ultraviolet radiation (UVR) is the most significant occupational carcinogenic exposure in terms of the number of workers exposed (i.e., outdoor workers). Consequently, solar UVR-induced skin cancers are among the most common forms of occupational malignancies that are potentially expected globally. This systematic review is registered in PROSPERO (CRD42021295221) and aims to assess the risk of cutaneous squamous cell carcinoma (cSCC) associated to occupational solar UVR exposure. Systematic searches will be performed in three electronic literature databases (PubMed/Medline, EMBASE, and Scopus). Further references will be retrieved by a manual search (e.g., in grey literature databases, internet search engines, and organizational websites). We will include cohort studies and case-control studies. Risk of Bias assessment will be conducted separately for case-control and cohort studies. The Grading of Recommendations Assessment, Development, and Evaluation (GRADE) will be used for the certainty of assessment. In case quantitative pooling is not feasible, a narrative synthesis of results will be performed.

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Prevention of Occupational Skin Cancer

Cited by 7 publications

References 52 publications

An Enhanced Transfer Learning Based Classification for Diagnosis of Skin Cancer

An Enhanced Transfer Learning Based Classification for Diagnosis of Skin Cancer

The Roles of Cloud-Based Systems on the Cancer-Related Studies: A Systematic Literature Review

Risk of cutaneous squamous cell carcinoma due to occupational exposure to solar ultraviolet radiation: Protocol for a systematic review and meta-analysis

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