scholarly journals AI-based localization and classification of skin disease with erythema

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ha Min Son ◽  
Wooho Jeon ◽  
Jinhyun Kim ◽  
Chan Yeong Heo ◽  
Hye Jin Yoon ◽  
...  
Keyword(s):  
Neural Network ◽  
Skin Diseases ◽  
Classification Model ◽  
Screening Tests ◽  
Sensitivity Score ◽  
Common Skin ◽  
Novel Method ◽  
Improved Performance ◽  
High Level

AbstractAlthough computer-aided diagnosis (CAD) is used to improve the quality of diagnosis in various medical fields such as mammography and colonography, it is not used in dermatology, where noninvasive screening tests are performed only with the naked eye, and avoidable inaccuracies may exist. This study shows that CAD may also be a viable option in dermatology by presenting a novel method to sequentially combine accurate segmentation and classification models. Given an image of the skin, we decompose the image to normalize and extract high-level features. Using a neural network-based segmentation model to create a segmented map of the image, we then cluster sections of abnormal skin and pass this information to a classification model. We classify each cluster into different common skin diseases using another neural network model. Our segmentation model achieves better performance compared to previous studies, and also achieves a near-perfect sensitivity score in unfavorable conditions. Our classification model is more accurate than a baseline model trained without segmentation, while also being able to classify multiple diseases within a single image. This improved performance may be sufficient to use CAD in the field of dermatology.

scholarly journals Contribution to image restoration using a neural network model

2002 ◽  
Vol Volume 1, 2002 ◽  
Author(s):  
Karim Achour ◽  
Nadia Zenati ◽  
Oualid Djekoune
Keyword(s):  
Neural Network ◽  
Image Restoration ◽  
Network Model ◽  
Neural Network Model ◽  
Hopfield Neural Network ◽  
Optimization Method ◽  
Nous Proposons ◽  
International Audience ◽  
High Level

International audience The reduction of the blur and the noise is an important task in image processing. Indeed, these two types of degradation are some undesirable components during some high level treatments. In this paper, we propose an optimization method based on neural network model for the regularized image restoration. We used in this application a modified Hopfield neural network. We propose two algorithms using the modified Hopfield neural network with two updating modes : the algorithm with a sequential updates and the algorithm with the n-simultaneous updates. The quality of the obtained result attests the efficiency of the proposed method when applied on several images degraded with blur and noise. La réduction du bruit et du flou est une tâche très importante en traitement d'images. En effet, ces deux types de dégradations sont des composantes indésirables lors des traitements de haut niveau. Dans cet article, nous proposons une méthode d'optimisation basée sur les réseaux de neurones pour résoudre le problème de restauration d'images floues-bruitées. Le réseau de neurones utilisé est le réseau de « Hopfield ». Nous proposons deux algorithmes utilisant deux modes de mise à jour: Un algorithme avec un mode de mise à jour séquentiel et un algorithme avec un mode de mise à jour n-simultanée. L'efficacité de la méthode mise en œuvre a été testée sur divers types d'images dégradées.

scholarly journals PSYCHOSOCIAL IMPACT OF COMMON SKIN DISORDERS – A REVIEW STUDY

2021 ◽  
Vol 9 (7) ◽  
pp. 1452-1456
Author(s):  
Manish Choudhari ◽  
Nikita Jamadari ◽  
Naresh Jain
Keyword(s):  
Quality Of Life ◽  
Atopic Dermatitis ◽  
Skin Disease ◽  
Skin Diseases ◽  
Fungal Infections ◽  
Acne Vulgaris ◽  
Well Being ◽  
Psychosocial Impact ◽  
Common Skin

Objective - To increase awareness of the psychosocial impact of Kushtha, Visarpa, Mukhadushika, Sheetpitta, Udarda and Kotha in Ayurveda and Acne vulgaris, Urticaria, Various types of fungal infections, Atopic dermatitis, Psoriasis etc. in modern point of view. Quality Of Evidence - A literature review was based on a MEDLINE search (1966 to 2000). Selected articles from the dermatologic and psychiatric literature, as well as other relevant medical journals, were reviewed and used as the basis for discussion of how skin disease affects patients’ lives and of appropriate management. Message - Dermatologic problems hurt patients’ quality of life. skin disease can produce stress, anxiety, anger, depression, low self-esteem, embarrassment, and other psychological, personal, professional and social life problems that affect patients’ lives in ways comparable to arthritis or other disabling illnesses, as well as showing a bidirectional relationship between skin disease and psychological distress. This review focuses on the effects of five common skin diseases seen by family physicians- Acne, Urticaria, Various types of fungal infections, Atopic dermatitis and Psoriasis. Conclusion - How skin disease affects psychosocial well-being is un- derappreciated. Increased understanding of the psychiatric comorbidity associated with skin disease and a biopsy- chosocial approach to management will ultimately improve patients’ lives. Keywords: Skin disease, Psychosocial Impact, Quality of life.

Dermatology

2018 ◽  
Author(s):  
Gemma Simcox
Keyword(s):  
Quality Of Life ◽  
Skin Disease ◽  
Skin Diseases ◽  
Systemic Disease ◽  
Management Plan ◽  
Dermatological Examination ◽  
Common Skin ◽  
Life Threatening ◽  
The Uk

Skin disease has a serious impact on an individual’s quality of life. It is well recognized that conditions such as psoriasis may have a similar impact on a patient’s quality of life to chronic diseases such as diabetes, hypertension, and depression. Skin problems account for approximately 20% of all patient consultations in primary care in the UK. It is important that clinicians are able to diagnose common skin diseases such as acne, eczema, psoriasis, and cutaneous malignancies and initiate an appropriate management plan. This requires the ability to take a full history and conduct a complete examination. A complete dermatological examination involves examination of the entire skin, mucous membranes, hair, and nails. The description of cutaneous pathologies should include the location and distribution of lesions. The morphology of a lesion or each component of a generalized eruption should be noted. Other organ systems may also need to be examined. The questions in this chapter will test your knowledge of the skin problems that are frequently encountered in non-specialist clinical practice. Other more rare skin disorders are also covered, either because they are potentially life-threatening or because they are a sign of systemic disease. The questions are designed to improve your ability to recognize the morphology and distribution of cutaneous physical signs. Hopefully you will find these questions stimulating and an aid to improving your knowledge of skin disease.

scholarly journals Identification of Skin Disease Using Deep Learning

2020 ◽  
pp. 1011-1116
Author(s):  
Shravani Kharat ◽  
Pooja Shinde ◽  
Preeti Malwadkar ◽  
Dipti Chaudhari
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Skin Disease ◽  
Skin Diseases ◽  
Disease Prediction ◽  
Learning Technology ◽  
Network Approach ◽  
Neural Network Approach ◽  
Prevention And Early Detection ◽  
Common Skin

Globally, skin diseases are among the most common health problems in all humans irrespective of age. Prevention and early detection of these diseases can improve the chance of surviving. This model illustrates the identification of skin diseases providing more objective and reliable solutions using deep learning technology and convolutional neural network approach. In this model, the system design, implementation and identification of common skin diseases such as acne, blister, eczema, warts etc. are explained. The system applies deep learning technology to train itself with various images of skin diseases from the Kaggle platform. The accuracy obtained by using deep learning is 83.23%. The main objective of this system is to achieve maximum accuracy of skin disease prediction. Moreover, if the disease is identified the system provides detailed information about the diseases along with home remedies.

Recurrence plot and convolutional neural network-based signal quality assessment method for using photoplethysmogram in mobile environment (Preprint)

2020 ◽  
Author(s):  
Hangsik Shin
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Quality Assessment ◽  
Feature Detection ◽  
Layer Structure ◽  
Recurrence Plot ◽  
Classification Model ◽  
Signal Quality ◽  
Predictive Values

BACKGROUND In clinical use of photoplethysmogram, waveform distortion due to motion noise or low perfusion may lead to inaccurate analysis and diagnostic results. Therefore, it is necessary to find an appropriate analysis method to evaluate the signal quality of the photoplethysmogram so that its wide use in mobile healthcare can be further increased. OBJECTIVE The purpose of this study was to develop a machine learning model that could accurately evaluate the quality of a photoplethysmogram based on the shape of the photoplethysmogram and the phase relevance in a pulsatile waveform without requiring a complicated pre-processing. Its performance was then verified. METHODS Photoplethysmograms were recorded for 76 participants (5 minutes for each participant). All recorded photoplethysmograms were segmented for each beat to obtain a total of 49,561 pulsatile segments. These pulsatile segments were manually labeled as 'good' and 'bad' classes and converted to a two-dimensional phase space trajectory image with size of 124 × 124 using a recurrence plot. The classification model was implemented using a convolutional neural network with a two-layer structure. It was verified through a five-fold cross validation. RESULTS As a result, the proposed model correctly classified 48,827 segments out of 49,561 segments and misclassified 734 segments, showing a balanced accuracy of 0.975. Sensitivity, specificity, and positive predictive values of the developed model for the test dataset with a ‘bad’ class classification were 0.964, 0.987, and 0.848, respectively. The area under the curve was 0.994. CONCLUSIONS The convolutional neural network model with recurrence plot as input proposed in this study can be used for signal quality assessment as a generalized model with high accuracy through data expansion. It has an advantage in that it does not require a complicated pre-processing or feature detection process. CLINICALTRIAL KCT0002080

scholarly journals Educational analytics. A new approach to improving the quality of the educational process

2020 ◽  
Author(s):  
D. Oskin ◽  
◽  
A. Oskin ◽  
Keyword(s):  
Online Education ◽  
Programming Language ◽  
Learning Analytics ◽  
Educational Process ◽  
Classification Model ◽  
New Approach ◽  
High Level ◽  
Student Classification

This article describes the trends in online education caused by the COVID-19 pandemic. The introduction of learning analytics into the educational process is substantiated. The main methods and tools of educational analytics are considered. Using a specific example, we will understand the construction and assessment of a student classification model using the high-level programming language Python.

Understanding Skin Conditions

2012 ◽  
Author(s):  
Steven J. Ersser
Keyword(s):  
Quality Of Life ◽  
Skin Diseases ◽  
Skin Care ◽  
Comprehensive Overview ◽  
Common Skin ◽  
The Common ◽  
Gp Visits ◽  
Genetic And Environmental Factors ◽  
Skin Conditions

The aim of this chapter is to provide nurses with the knowledge to be able to assess, manage, and care for people with skin conditions in an evidence-based and person-centred way. The chapter will provide a comprehensive overview of the commonest skin diseases and their causes before exploring best practice to assess and help patients to manage skin conditions. Nursing priorities are highlighted throughout, and the nursing management of the symptoms and common health problems associated with skin conditions can be found in Chapters 19, 20, 21, 24, 27, and 28 on skin care and the maintenance of skin hygiene, skin barrier integrity, the prevention of skin breakdown, and wound management, respectively. Skin care is a fundamental area of nursing responsibility. The skin, or integumentary system, is the largest organ of the body and has significant protective and thermoregulatory functions. Skin disease is common, accounting for approximately 24% of GP visits (Schofield et al., 2009). It may have a major psychosocial impact on a person’s quality of life through its influence on appearance, body image, and self-esteem. This chapter introduces you to the common skin diseases that you are likely to encounter when caring for adult patients and outlines the nursing problems that you will need to manage. The cause or aetiology of common skin conditions lies with the interaction between genetic and environmental factors. For example, a child’s eczema is influenced by his or her genotype and his or her exposure to environmental allergens. Within the UK population, 23–25% have a skin problem at some time in their lives that can benefit from medical care (Schofield et al., 2009). Skin problems are the commonest reason for consulting a GP, with 6% referred for specialist advice. As such, all registered nurses should have the knowledge and skills to manage the common conditions. The commonest skin conditions in the Western hemisphere are chronic inflammatory skin diseases (CISDs), such as eczema. In developing countries, the common conditions are infections and infestations. The quality-of-life impact of CISDs can exceed that for life-threatening conditions such as cancer (Rapp et al., 1999; Kingman, 2005).

scholarly journals Real-Time Hand Gesture Spotting and Recognition Using RGB-D Camera and 3D Convolutional Neural Network

2020 ◽  
Vol 10 (2) ◽  
pp. 722 ◽  
Author(s):  
Dinh-Son Tran ◽  
Ngoc-Huynh Ho ◽  
Hyung-Jeong Yang ◽  
Eu-Tteum Baek ◽  
Soo-Hyung Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Human Computer Interaction ◽  
Real Time ◽  
Gesture Recognition ◽  
Hand Gesture Recognition ◽  
Video Gaming ◽  
Hand Gesture ◽  
Novel Method ◽  
High Level ◽  
Computer Interaction

Using hand gestures is a natural method of interaction between humans and computers. We use gestures to express meaning and thoughts in our everyday conversations. Gesture-based interfaces are used in many applications in a variety of fields, such as smartphones, televisions (TVs), video gaming, and so on. With advancements in technology, hand gesture recognition is becoming an increasingly promising and attractive technique in human–computer interaction. In this paper, we propose a novel method for fingertip detection and hand gesture recognition in real-time using an RGB-D camera and a 3D convolution neural network (3DCNN). This system can accurately and robustly extract fingertip locations and recognize gestures in real-time. We demonstrate the accurateness and robustness of the interface by evaluating hand gesture recognition across a variety of gestures. In addition, we develop a tool to manipulate computer programs to show the possibility of using hand gesture recognition. The experimental results showed that our system has a high level of accuracy of hand gesture recognition. This is thus considered to be a good approach to a gesture-based interface for human–computer interaction by hand in the future.

scholarly journals Quality of life in Montenegrin pupils with acne

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0250155
Author(s):  
Milena Ražnatović Đurović ◽  
Milica Đurović ◽  
Janko Janković ◽  
Slavenka Janković
Keyword(s):  
Quality Of Life ◽  
Skin Disease ◽  
Skin Diseases ◽  
Life Quality ◽  
Total Sample ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Common Skin ◽  
The Mean

Background Acne is a common skin disease that can affect a person’s quality of life (QoL), self-esteem, and mood in an adverse manner. The aim of the current study was to assess QoL among Montenegrin pupils with acne. Methods This cross-sectional survey was conducted over October and November 2020 in four randomly selected secondary schools in Podgorica, Montenegro. All 500 pupils were asked to fill in a short questionnaire which included questions on age, sex, presence of acne, and for those with acne their duration and location, visits to doctors, presence of any other coexisting skin disease, and family history of acne. Only pupils who self-reported acne were asked to complete the Children’s Dermatology Life Quality Index (CDLQI) and the Cardiff Acne Disability Index (CADI). Results Self reported acne were presented in 49.8% (249/500) of all pupils. The mean CDLQI score of the total sample was 4.27 ± 5.13. Overall, the CDLQI domains that were most affected by acne were symptoms and feelings (mean score 1.49 ± 1.43), leisure (mean score 0.94 ± 1.72), and treatment (0.66 ± 0.79). The mean total CADI score was 3.53 ± 3.11 which was higher in girls (4.07 ± 3.11) than in boys (2.90 ± 3.00). There was good correlation between the two questionnaires (Rho = 0.76; P < 0.01). According to multiple linear regressions, higher overall CDLQI score was found in pupils with acne who reported other skin diseases, while girls, pupils who reported both acne on face and back, and who had any concomitant skin disease had higher CADI total score. Conclusions Acne affects QoL of young adolescents in Montenegro with greater impact in girls. Our findings should point out the importance of timely diagnosis, treatment, and education of adolescents with acne.

scholarly journals Atopic dermatitis: advancement and problems in understanding the essence of the disease and its treatment

2021 ◽  
Vol 98 (9-10) ◽  
pp. 650-655
Author(s):  
N. A. Voronkova ◽  
E. V. Dontsova ◽  
L. A. Novikova ◽  
L. N. Borzunova
Keyword(s):  
Atopic Dermatitis ◽  
Skin Diseases ◽  
Skin Barrier ◽  
Regulatory Peptides ◽  
Cochrane Library ◽  
Gene Encoding ◽  
Common Skin ◽  
The Cochrane Library

The review represents the analysis of modern data on the pathogenesis and methods of treatment of atopic dermatitis (AtD). The literature search was carried out using the Scopus, Web of Science, MedLine, The Cochrane Library, EMBASE, e-library databases. AtD is one of the most common skin diseases, aff ecting about 20% of children and 5% of adults in advanced countries. The disease is multifactorial by its etiology. Among the genetic factors, the main attention is paid to the mutation of the gene encoding the synthesis of fi laggrin-protein involved in the functioning of the skin barrier. The role of cytokines regulating the synthesis of IgE — interleukins (IL) -4, -5, -12, -13, -31 is studied in the genesis of immune disorders in AtD. Steady-state stress accompanying pruritic dermatitis contributes to the development of anxiodepressive сonditions degrades quality of life, and stress-related increase of cortisol level may be essential in impairing the barrier function of the skin. Among the new approaches to the treatment of patients with AtD, the possibilities of using Selank, which represents the group of regulatory peptides and narrow-band phototherapy of the 311 nm range, are discussed.

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