scholarly journals Post-inflammatory hyperpigmentation of the skin

2021 ◽  
Author(s):  
Dr. Carolina Diamandis ◽  
David Seideman ◽  
Aleksandros Makris
Keyword(s):  
Skin Cancer ◽  
Skin Lesions ◽  
Inflammatory Skin ◽  
Cosmetic Medicine ◽  
Lack Of Knowledge

Post-inflammatory hyperpigmentation is the most benign cause of brownish discolorations of the skin. For this reason it has been little researched. In times of increasing treatments in the field of cosmetic medicine and simultaneous increased attention to the issue of skin cancer, a lack of knowledge may lead to over-treatment of post-inflammatory skin lesions.

A qualitative exploration of parental perceptions regarding children’s sun exposure, sun protection, and sunburn

2020 ◽  
Author(s):  
Karlijn Thoonen ◽  
Liesbeth van Osch ◽  
Rowan Drittij ◽  
Hein de Vries ◽  
Francine Schneider
Keyword(s):  
Skin Cancer ◽  
Sun Exposure ◽  
Structured Interview ◽  
Sun Protection ◽  
Parental Perceptions ◽  
Skin Cancer Prevention ◽  
Protection Measure ◽  
Sun Safety ◽  
Protection Measures ◽  
Lack Of Knowledge

Sun protection among children is of utmost importance since sunburn in early life is a major risk factor for skin cancer development. Because parents play a vital role in enhancing sun safety among children, this study explored parental perceptions concerning sun exposure, sun protection behaviors, and sunburn in children. Additionally, the context in which children experience sunburn in order to assist the development, optimization, and targeting of sun safety interventions for parents is revealed.A qualitative study design, using a semi-structured interview guide addressing several themes (e.g. sun exposure, sun protection, and sunburn experiences), was used. Data were collected in the Netherlands in the fall of 2019. Parents were recruited via purposive sampling at schools, youth services centers, and social media. In total, 26 interviews were performed. Execution, transcription, and coding of the interviews was done by two researchers, using the qualitative analyzing program Nvivo (interrater reliability of d =.84). Comprehensive findings concerning various themes were retrieved. It was found that sunburn was frequently prevalent among children, even though all parents reported using at least one sun protection measure. Parents were often unaware of their child’s sunburn and its severity. Regarding sun protection measures, parents demonstrated an overreliance on sunscreen, often failing to adequately protect their children’s skin. Water-related activities, a lack of shade, and lack of knowledge regarding UV-index were often related to sunburn. Moreover, unexpected sun exposure or longer exposure duration than initially planned were reported as challenging situations. The majority of parents had positive perceptions regarding tanned skin for both themselves as for children.This study provides directions for future skin cancer prevention efforts targeted at both parents and their children. Since a lack of knowledge regarding sufficient sun protection measures and sunburn occurrence in various situations was reported, educational efforts are warranted. Additionally, focusing on clothing, shade-seeking, and adequate sunscreen use is recommended to increase children’s sun safety. By intervening in the physical environment as well (e.g. providing shady areas), sun protection barriers can be reduced. Lastly, the general positive attitude toward tanned skin evident in this study is certainly worthy of attention in future interventions.

scholarly journals Screening for Melanoma and Other Skin Cancer Shows a Higher Early Melanoma Incidence: Social Educational Program “Life Fear-Free”

2021 ◽  
Vol 8 (1) ◽  
pp. 54-68
Author(s):  
Lev Demidov ◽  
Igor Samoylenko ◽  
Nina Vand ◽  
Igor Utyashev ◽  
Irina Shubina ◽  
...  
Keyword(s):  
Skin Cancer ◽  
Screening Program ◽  
Excisional Biopsy ◽  
Skin Lesions ◽  
Detection Methods ◽  
Non Melanoma Skin Cancer ◽  
The Social ◽  
Educational Campaign ◽  
Skin Cancer Detection ◽  
The Impact

Background: The screening program Life Fear-Free (LFF) aimed at early diagnosis of cutaneous melanoma (CM) was introduced in Samara, Chelyabinsk, Yekaterinburg, and Krasnodar (Russia) in 2019. Objectives: To analyze the impact of the program on early CM and non-melanoma skin cancer (NMSC) detection. Methods: According to the social educational campaign, people were informed about CM risk factors and symptoms and were invited for skin examination. The program planned to involve 3200 participants in total. Participants with suspicious lesions were invited for excisional biopsy. Results: 3143 participants, including 75.4% women, were examined for skin lesions. The average age of the participants was 43.7 years. Mostly skin phototypes II and III were registered (48.2% and 41.0%, respectively); 3 patients had CM, 15 had basal cell carcinoma, and 1 had Bowen’s disease, which were confirmed histologically. All detected melanomas had Breslow’s thickness of 1 mm. Conclusion: The participants showed high interest in early skin cancer detection programs. The incidence rate of CM and NMSCs among the program participants was higher than in general public. The early disease grade was proven for the detected CMs and NMSCs. The study has shown that it is important to continue such programs.

scholarly journals Teaching Skin Cancer Detection to Medical Students Using a Dermoscopic Algorithm

PRiMER ◽  
2021 ◽  
Vol 5 ◽  
Author(s):  
Peggy R. Cyr ◽  
Wendy Craig ◽  
Hadjh Ahrns ◽  
Kathryn Stevens ◽  
Caroline Wight ◽  
...  
Keyword(s):  
Primary Care ◽  
Medical Students ◽  
Skin Cancer ◽  
Primary Care Physicians ◽  
Survival Rates ◽  
Family Physicians ◽  
Skin Lesions ◽  
Posttest Score ◽  
Malignant Skin ◽  
Attending Physicians

Introduction: Early detection of melanoma skin cancer improves survival rates. Training family physicians in dermoscopy with the triage amalgamated dermoscopic algorithm (TADA) has high sensitivity and specificity for identifying malignant skin neoplasms. In this study we evaluated the effectiveness of TADA training among medical students, compared with practicing clinicians. Methods: We incorporated the TADA framework into 90-minute workshops that taught dermoscopy to family physicians, primary care residents, and first- and second-year medical students. The workshop reviewed the clinical and dermoscopic features of benign and malignant skin lesions and included a hands-on interactive session using a dermatoscope. All participants took a 30-image pretest and a different 30-image posttest. Results: Forty-six attending physicians, 25 residents, and 48 medical students participated in the workshop. Mean pretest scores were 20.1, 20.3, and 15.8 for attending physicians, resident physicians and students, respectively (P<.001); mean posttest scores were 24.5, 25.9, and 24.1, respectively (P=.11). Pre/posttest score differences were significant (P<.001) for all groups. The medical students showed the most gain in their pretest and posttest scores. Conclusion: After short dermoscopy workshop, medical students perform as well as trained physicians in identifying images of malignant skin lesions. Dermoscopy training may be a valuable addition to the medical school curriculum as this skill can be used by primary care physicians as well as multiple specialists including dermatologists, gynecologists, otolaryngologists, plastic surgeons, and ophthalmologists, who often encounter patients with concerning skin lesions.

scholarly journals Intelligent Dermatologist Tool for Classifying Multiple Skin Cancer Subtypes by Incorporating Manifold Radiomics Features Categories

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Omneya Attallah ◽  
Maha Sharkas
Keyword(s):  
Skin Cancer ◽  
Binary Classification ◽  
Survival Rates ◽  
Skin Lesions ◽  
Accurate Diagnosis ◽  
Computer Assisted ◽  
Discrete Wavelet ◽  
Cancer Subtypes ◽  
Low Level ◽  
High Level

The rates of skin cancer (SC) are rising every year and becoming a critical health issue worldwide. SC’s early and accurate diagnosis is the key procedure to reduce these rates and improve survivability. However, the manual diagnosis is exhausting, complicated, expensive, prone to diagnostic error, and highly dependent on the dermatologist’s experience and abilities. Thus, there is a vital need to create automated dermatologist tools that are capable of accurately classifying SC subclasses. Recently, artificial intelligence (AI) techniques including machine learning (ML) and deep learning (DL) have verified the success of computer-assisted dermatologist tools in the automatic diagnosis and detection of SC diseases. Previous AI-based dermatologist tools are based on features which are either high-level features based on DL methods or low-level features based on handcrafted operations. Most of them were constructed for binary classification of SC. This study proposes an intelligent dermatologist tool to accurately diagnose multiple skin lesions automatically. This tool incorporates manifold radiomics features categories involving high-level features such as ResNet-50, DenseNet-201, and DarkNet-53 and low-level features including discrete wavelet transform (DWT) and local binary pattern (LBP). The results of the proposed intelligent tool prove that merging manifold features of different categories has a high influence on the classification accuracy. Moreover, these results are superior to those obtained by other related AI-based dermatologist tools. Therefore, the proposed intelligent tool can be used by dermatologists to help them in the accurate diagnosis of the SC subcategory. It can also overcome manual diagnosis limitations, reduce the rates of infection, and enhance survival rates.

scholarly journals SKIN LESION CLASSIFICATION FROM DERMOSCOPY AND CLINICAL IMAGES WITH A DEEP LEARNING APPROACH

2021 ◽  
Vol 9 (10) ◽  
pp. 1294-1300
Author(s):  
Aigli Korfiati ◽  
◽  
Giorgos Livanos ◽  
Christos Konstandinou ◽  
Sophia Georgiou ◽  
...  
Keyword(s):  
Deep Learning ◽  
Skin Cancer ◽  
Medical Image ◽  
Skin Lesions ◽  
Learning Approaches ◽  
Early Screening ◽  
Clinical Images ◽  
Cad Systems ◽  
Learning Classifier ◽  
Aided Diagnosis

Computer-aided diagnosis (CAD) systems based on deep learning approaches are now feasible due to the availability of big data and the availability of powerful computational resources.The medical image-based CAD systems are of great interest in numerous diseases, but especially for skin cancer diagnosis, deep learning models have been mostly developed for dermoscopy images. Models for clinical images are few, mainly due to the unavailability of big volumes of relevant data. However, CAD systems able to classify skin lesions from clinical images would be of great valueboth for the population and clinicians as an initial early screening of lesions that would leadpatients to visiting a dermatologist in case of suspicious lesions. This is even more pronounced in areas where there is lack of dermoscopy instruments. Thus, in this paper, we aimed to build a classifier based on bothdermoscopy and clinical images able to discriminate skin cancer from skin lesions. The classification is made among three benign and two malignant categories, which include Nevus, Benign but not nevus, Benign but suspicious for malignancy, Melanoma and Non-Melanocytic Carcinoma.The proposed deep learning classifier achieves an Area Under Curve ranging between 0.75 and 0.9 for the five examined categories.

Dermatology

2019 ◽  
pp. 213-230
Keyword(s):  
Skin Cancer ◽  
Acne Vulgaris ◽  
Systemic Disease ◽  
Skin Lesions ◽  
Punch Biopsy ◽  
Stevens Johnson Syndrome ◽  
Clinical Approach ◽  
Dermatology Clinic ◽  
Skin Cancers ◽  
Johnson Syndrome

Dermatology is the study of the skin, hair, nails, and oral and genital mucus membranes and the diseases affecting them. It is predominantly an outpatient specialty. This chapter explains the common terminology used to describe skin lesions and dermatoses. The commonest conditions encountered in the dermatology clinic are described: eczema, psoriasis, pyoderma gangrenosum, skin cancers (basal cell skin cancer, squamous cell skin cancer, malignant melanoma), acne vulgaris and bullous disorders, in addition to dermatological manifestations of systemic disease such as vasculitis. Emergency presentations such as Stevens–Johnson syndrome/toxic epidermal necrolysis, anaphylaxis, and necrotizing fasciitis are outlined. A practical guide to common dermatological procedures such as punch biopsy, and a clinical approach to the dermatological patient are included.

Proposed Threshold Algorithm for Accurate Segmentation for Skin Lesion

2015 ◽  
Vol 4 (2) ◽  
pp. 40-47
Author(s):  
T. Y. Satheesha ◽  
D. Sathyanarayana ◽  
M. N. Giri Prasad
Keyword(s):  
Skin Cancer ◽  
Skin Lesion ◽  
Skin Lesions ◽  
Large Set ◽  
Threshold Values ◽  
Diagnostic Systems ◽  
Automated Diagnosis ◽  
Algorithm Efficiency ◽  
Threshold Algorithms ◽  
Threshold Technique

Automated diagnosis of skin cancer can be easily achieved only by effective segmentation of skin lesion. But this is a highly challenging task due to the presence of intensity variations in the images of skin lesions. The authors here, have presented a histogram analysis based fuzzy C mean threshold technique to overcome the drawbacks. This not only reduces the computational complexity but also unifies advantages of soft and hard threshold algorithms. Calculation of threshold values even the presence of abrupt intensity variations is simplified. Segmentation of skin lesions is easily achieved, in a more efficient way in the following algorithm. The experimental verification here is done on a large set of skin lesion images containing every possible artifacts which highly contributes to reversed segmentation outputs. This algorithm efficiency was measured based on a comparison with other prominent threshold methods. This approach has performed reasonably well and can be implemented in the expert skin cancer diagnostic systems

A Study on Automatic Segmentation and Classification of Skin Lesions in Dermoscopic Images

2015 ◽  
Vol 6 (4) ◽  
pp. 51-61
Author(s):  
Ebtihal Abdullah Al-Mansour ◽  
Arfan Jaffar
Keyword(s):  
Skin Cancer ◽  
Imaging Techniques ◽  
Automatic Segmentation ◽  
Survival Rates ◽  
Skin Lesions ◽  
Incidence Rates ◽  
Automated System ◽  
Final State ◽  
Survey Paper ◽  
Real Scenario

Malignant Melanoma is one of the rare and the deadliest form of skin cancer if left untreated. Death rate due to this cancer is three times more than all other skin-related malignancies combined. Incidence rates of melanoma have been increasing, especially among young adults, but survival rates are high if detected early. There is a need for an automated system to assess a patient's risk of melanoma using digital dermoscopy, that is, a skin imaging technique widely used for pigmented skin lesion inspection. Although many automated and semi-automated methods are available to diagnose skin cancer but each has its own limitations and there is no final, state-of-the art technique to date which is able to be implemented in real scenario. This survey paper is based on techniques used to segment the skin cancer, analysis of their merits and demerits and their applications on advanced imaging techniques.

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