Deep Learning Based Automated Diagnosis of Skin Diseases Using Dermoscopy

Biomedical image analysis has been exploited considerably by recent technology involvements,carrying about a pattern shift towards‘automation’and‘error free diagnosis’classification methods with markedly improved accurate diagnosis productivity and cost effectiveness.This paper proposes an automated deep learning model to diagnose skin disease at an early stage by using Dermoscopy images.The proposed model has four convolutional layers,two maxpool layers,one fully connected layer and three dense layers.All the convolutional layers are using the kernel size of 3∗3 whereas the maxpool layer is using the kernel size of 2∗2.The dermoscopy images are taken from the HAM10000 dataset.The proposed model is compared with the three different models of ResNet that are ResNet18,ResNet50 and ResNet101.The models are simulated with 32 batch size and Adadelta optimizer.The proposed model has obtained the best accuracy value of 0.96 whereas the ResNet101 model has obtained 0.90,the ResNet50 has obtained 0.89 and the ResNet18 model has obtained value as 0.86.Therefore,features obtained from the proposed model are more capable for improving the classification performance of multiple skin disease classes.This model can be used for early diagnosis of skin disease and can also act as a second opinion tool for dermatologists.

Sequence Analysis of Attachment Gene of Lumpy Skin Disease and Sheep Poxviruses

In Egypt,protection of cattle against lumpy skin disease (LSD) was carried out using a sheep poxvirus (Kenyan strain) vaccination strategy.In the present study 15 skin nodules from LSD suspected cows and 5 scab samples from sheep pox (SP) suspected sheep were collected.Hyperimmune rabbit sera to Lumpy skin disease virus (LSDV)/Ismailyia88 strain and sheep pox virus (SPV)/ Kenyan vaccinal strain were prepared.The causative agent in the collected samples was identified using immunoflourescence (IF) and immunoperoxidase techniques.Of the 15 skin nodules suspected of LSD,10 showed a positive reaction and 3 out of 5 skin scabs suspected of sheeppox were found to be positive.An antigenic correlation between field skin isolate of LSDV,tissue culture adapted LSDV/Ismailyia88 strain,field skin isolate of SPV and SPV/Kenyan vaccinal strain was studied using prepared hyperimmune sera.Also,nucleotide sequence of the PCR amplified attachment gene fragments of field skin isolate of LSDV,tissue culture adapted LSDV/Ismailyia88 strain,field skin isolate of SPV and SPV /Kenyan vaccinal strain were compared.The results revealed that the four used viruses were antigenically identical.Sequence analysis indicated that field skin LSDV isolate is more related to tissue culture adapted LSDV/Ismailyia88 strain than to vaccinal SPV/ Kenyan strain and the skin isolate of SPV is more closely related to field skin isolate of LSDV than to SPV/Kenyan vaccinal strain.Thus,further study should be applied on the advantage of a LSD vaccine prepared from LSDV in protection of cattle against LSD compared to the commonly used sheep pox vaccine.

A summary about dendritic cells in skin diseases

Dendritic cellls (DCs) comprise an es se ntial component of the immune system, are crucial in the initiation of antigen s pecific immune responses. In this summary we focus on summarizing on the central role of DCs in skin diseases: Bullous dermatoses, Dermatitis, Psoriasis, Lichen Planus , Graft-versus-host disease, Connect Tissue Diseases, Virus Diseases, Fungi Diseases, HIV, Urticaria, Urticaria pigmentosa, Mastocytosis, Tumour, Sola r dermatoses. Moreover,in this summary we review the distribution and phenotype of DCs in human skin. Markers and phenotyps 's study have provided strong suppo rt for a concept in which DCs play an important role in the pothogenesis of som e skin diseases.

Opportunities and Challenges:Classification of Skin Disease Based on Deep Learning

Deep learning has become an extremely popular method in recent years,and can be a powerful tool in complex,prior-knowledge-required areas,especially in the field of biomedicine,which is now facing the problem of inadequate medical resources.The application of deep learning in disease diagnosis has become a new research topic in derma-tology.This paper aims to provide a quick review of the classification of skin disease using deep learning to summa-rize the characteristics of skin lesions and the status of image technology.We study the characteristics of skin disease and review the research on skin disease classification using deep learning.We analyze these studies using datasets,data processing,classification models,and evaluation criteria.We summarize the development of this field,illustrate the key steps and influencing factors of dermatological diagnosis,and identify the challenges and opportunities at this stage.Our research confirms that a skin disease recognition method based on deep learning can be superior to professional dermatologists in specific scenarios and has broad research prospects.

In Vitro Study on the Effect of Bee Venom on Some Cell Lines and Lumpy Skin Disease Virus

Bee venom （BV） was used from long time ago in the medical field as treatment of chronic joint affections. In the recent decades, the screening process of new sources of antimicrobials discovers its high advantageous characteristics for combating various types of microbes, as well as trials to discover its anti-cancer medicinal fields. Lumpy skin disease virus （LSDV） causes disease in cattle of economic importance, and this work aimed to find treatment as well as alternative inactivant for LSDV. The use of bee venom as antiviral was experimented in this work and exhibited satisfied inhibitory effects on LSDV, meanwhile, the antigenic properties was still intact. The viability of virus was tested in tissue culture cells lines and in embryonated chicken eggs. According to doses and time of exposure, the cell lines of Hep-2 （human larynx carcinoma） and MCF7 （breast carcinoma cell line） were treated with different concentrations of BV and examined after 24 h post-inoculation. The Hep-2 and MCF7 cell lines were treated with various concentrations of BV in descending doses as follow： 25, 20, 15, 10, 5 and 0.5 ug/mL of BV. Then bee venom pathological effects on Hep-2 cells and MCF7 cells were observed, such as apoptosis, retarded growths and cytolysis. The results indicate the possibilities of using bee venom as anti-neoplastic and antiviral.

Androgens/Androgen Receptor in the Management of Skin Diseases

Beyond regulation of male sexual function, the increasing evidence shows that androgens and androgen receptor (AR) have a variety of physiological and pathological effects on the skin. Skin cells express all androgen metabolizing enzymes that are required for independent skin androgen synthesis and the development of hyperandrogenic related disorders such as acne, hirsutism and androgenetic alopecia. Targeting various elements of androgen function and metabolism is the major goal of medication design for the treatment of androgen-related diseases. Antiandrogen drugs such as clascoterone, flutamide could improve conditions. Even though the involvement of androgens and AR in skin diseases has been investigated for a long time, their molecular mechanisms in skin disorders remain largely insufficient. In this review, recent studies and advances on the role of androgens/AR in several skin-related diseases and their therapeutics are systematically summarized.

Clinical Efficacy and Adverse Reactions of Photodynamic Therapy in the Treatment of Malignant Skin Tumors and Precancerous Skin Diseases

The purpose of the study was to investigate the efficacy and safety of photodynamic therapy in the treatment of malignant skin tumors and precancerous skin diseases.First of all,100 patients with malignant skin tumor and pre-cancer skin disease admitted to our hospital from January 20,2018 to September 18,2018 were selected,and all of them adopted photodynamic therapy.The 100 patients included 40 cases of basal cell carcinoma,14 cases of squamous cell carcinoma,7 cases of proliferative erythema,9 cases of Bowen’s disease and 39 cases of solar keratosis.Among the 100 patients,77 cases had excellent curative effect,18 cases had good effect and 5 cases had poor effect,and the overall effective rate was 95%(95/100).After 6 months of follow-up,5 cases of basal cell carcinoma,2 cases of squamous cell carcinoma,0 cases of proliferative erythra,1 case of Bowen’s disease and 3 cases of solar keratosis recurred,with an overall recurrence rate of 14%(14/100).The adverse reactions of pruritus were 90%(90/100),81%(81/100),90%(90/100)and 3%(3/100).Therefore,the application of photodynamic therapy in the treatment of malignant skin tumors and precancerous skin diseases is not only effective,but also safe and not easy to relapse.

Fecal microbiota transplant and dermatologic disorders:A retrospective cohort study assessing the gut microbiome’s role in skin disease

BACKGROUND There is indication that fecal microbiota transplant(FMT)has the potential to alter the course of chronic skin disease,but few studies have investigated this phenomenon beyond case reports.Research with larger sample sizes is needed to provide a more thorough assessment of possible associations and to establish a broader foundation upon which to base hypotheses.AIM To identify associations between FMT and skin conditions,particularly infectious and inflammatory etiologies,and the role of dermatology post-FMT.METHODS We conducted a retrospective cohort study involving a chart review of all patients whom received FMT between January 2013 and December 2019 at a single academic medical center.Dermatologic follow-up was assessed for the two years after FMT or through March 2020 for more recent procedures.Dermatologic diagnoses and visits within the study time frame were recorded and assessed for trends.This study was exploratory in nature.Descriptive statistics were calculated,and the t-test,Pearson’s chi-squared test,and Fisher’s exact test were used to calculate P values.RESULTS Median age was 38(range,17-90).In total,109 patients who underwent 111 fecal microbiota transplant events were included.Twenty-six events(23.4%)involved a dermatology office visit post-procedure,and of these events,20 out of the 26(76.9%)had an infectious or inflammatory skin condition.The mean time to first visit was 10.0(±7.0)mo.The most common diagnoses were dermatophyte,wart(s),and dermatitis,though no specific diagnoses predominated in a way indicating FMT had a significant impact.More patients with a post-FMT skin disease diagnosis had a history of Crohn’s disease compared to those without(P=0.022),but results could be affected by a small sample size.CONCLUSION Our study is limited by its retrospective nature,but the findings allow a glimpse at dermatologic conditions post-FMT.Few significant associations were found,but potential associations between FMT and skin disease should be further investigated,preferably in prospective studies,to identify how FMT might be of use for treating infectious and inflammatory skin diseases.

Early SkinDiseaseIdentification Using Deep Neural Network

Skin lesions detection and classification is a prominent issue and difficult even for extremely skilled dermatologists and pathologists.Skin disease is the most common disorder triggered by fungus,viruses,bacteria,allergies,etc.Skin diseases are most dangerous and may be the cause of serious damage.Therefore,it requires to diagnose it at an earlier stage,but the diagnosis therapy itself is complex and needs advanced laser and photonic therapy.This advance therapy involvesfinancial burden and some other ill effects.Therefore,it must use artificial intelligence techniques to detect and diagnose it accurately at an earlier stage.Several techniques have been proposed to detect skin disease at an earlier stage but fail to get accuracy.Therefore,the primary goal of this paper is to classify,detect and provide accurate information about skin diseases.This paper deals with the same issue by proposing a high-performance Convolution neural network(CNN)to classify and detect skin disease at an earlier stage.The complete meth-odology is explained in different folds:firstly,the skin diseases images are pre-processed with processing techniques,and secondly,the important feature of the skin images are extracted.Thirdly,the pre-processed images are analyzed at different stages using a Deep Convolution Neural Network(DCNN).The approach proposed in this paper is simple,fast,and shows accurate results up to 98%and used to detect six different disease types.

The critical importance of epigenetics in autoimmune-related skin diseases

Autoimmune-related skin diseases are a group of disorders with diverse etiology and pathophysiology involved in autoimmunity.Genetics and environmental factors may contribute to the development of these autoimmune disorders.Although the etiology and pathogenesis of these disorders are poorly understood,environmental variables that induce aberrant epigenetic regulations may provide some insights.Epigenetics is the study of heritable mechanisms that regulate gene expression without changing DNA sequences.The most important epigenetic mechanisms are DNA methylation,histone modification,and noncoding RNAs.In this review,we discuss the most recent findings regarding the function of epigenetic mechanisms in autoimmune-related skin disorders,including systemic lupus erythematosus,bullous skin diseases,psoriasis,and systemic sclerosis.These findings will expand our understanding and highlight the possible clinical applications of precision epigenetics approaches.

Modelling epidemic growth models for lumpy skin disease cases in Thailand using nationwide outbreak data,2021e2022

Lumpy skin disease(LSD)is a transboundary disease affecting cattle and has a detrimental effect on the cattle industries in numerous countries in Africa,Europe and Asia.In 2021,LSD outbreaks have been reported in almost all of Thailand's provinces.Indeed,fitting LSD occurrences using mathematical models provide important knowledge in the realm of animal disease modeling.Thus,the objective of this study is to fit the pattern of daily new LSD cases and daily cumulative LSD cases in Thailand using mathematical models.The first-and second-order models in the forms of Lorentzian,Gaussian and Pearson-type VII models are used to fit daily new LSD cases whereas Richard's growth,Boltzmann sigmoidal and Power-law growth models are utilized to fit the curve of cumulative LSD cases.Based on the root-mean-squared error(RMSE)and Akaike information criterion(AIC),results showed that both first and second orders of Pearson-type VII models and Richard's growth model(RGM)were fit to the data better than other models used in the present study.The obtained models and their parameters can be utilized to describe the LSD outbreak in Thailand.For disease preparedness purposes,we can use the first order of the Pearson-type VII model to estimate the time of maximum infected cases occurring when the growth rate of infected cases starts to slow down.Furthermore,the period when the growth rate changes at a slower rate,known as the inflection time,obtained from RGM allows us to anticipate when the pandemic has peaked and the situation has stabilized.This is the first study that utilizes mathematical methods to fit the LSD epidemics in Thailand.This study offers decision-makers and authorities with valuable information for establishing an effective disease control strategy.

Isolation and Identification of the Pathogen Causing Skin Ulcer Disease in Cynoglossus semilaevis Günther and Its Sensitivity to Chinese Herbal Medicine

In this study, the pathogen causing skin ulcer disease in Cynoglossus semilaevis Gunther was isolated for morphological observation, physiological and bio- chemical identification. According to the result, the isolated pathogen was identified as Vibrio harveyi. The results of recurrent infection of C. semilaevis Gonther showed that the pathogen was strongly pathogenic to C. semilaevis Gunther. In or- der to explore the pathogenesis, outer membrane protein （OMP） gene of C. semi- laevis Gunther was detected by PCR. The results showed that all the three repre- sentative strains harbored OMP gene. According to the results of sensitivity test of the pathogen to Chinese herbal medicine, Galla Chinensis, Fructus Mume, Fructus Hippophae and Lignum Sappan exerted strong antibacterial effects against V. harveyr, Pericarpium Granati exhibited slight antibacterial effect against V. harveyi; Pericarpium Citri Reticulatae, Rhizoma Acori GramineL Herba Houttuyniae, Herba Portulacae, Herba Andrographis, Eucalyptus globulus Labill. and Herba Menthae Heplocalycis had little effect on V. harveyi. Galla Chinensis, Fructus Mume, Fructus Hippophae and Lignum Sappan were prepared into three prescriptions, among which prescription 1 （Galla Chinensis ＋ Fructus Mume） exhibited the strongest antibacterial effect.

Skin diseases in the Da Qing Diabetes Study:a cross-sectional study

Background:The prevalence of skin diseases and diabetes mellitus(DM)are prominent around the world.The current scope of knowledge regarding the prevalence of skin diseases and comorbidities with type 2 DM(T2DM)is limited,leading to limited recognition of the correlations between skin diseases and T2DM.Methods:We collected 383 subjects from the Da Qing Diabetes Study during the period from July 9th to September 1st,2016.The subjects were categorized into three groups:Normal glucose tolerance(NGT),impaired glucose tolerance(IGT),and T2DM.The prevalence and clinical characteristics of skin diseases were recorded and investigated.Results:In this cross-sectional study,383 individuals with ages ranging from 53 to 89-year-old were recruited.The overall prevalence of skin diseases was 93.5%,and 75.7%of individuals had two or more kinds of skin diseases.Additionally,there were 47 kinds of comorbid skin diseases in patients with T2DM,of which eight kinds of skin diseases had a prevalence>10%.The prevalence of skin diseases in NGT,IGT,and T2DM groups were 93.3%,91.5%,and 96.6%,respectively;stratified analysis by categories showed a statistically significant difference in"disturbances of pigmentation"and"neurological and psychogenic dermatoses".The duration of T2DM also significantly associated with the prevalence of"disturbances of pigmentation"and"neurological and psychogenic dermatoses".Subsequently,the prevalence of"disturbances of pigmentation"was higher in males than females in NGT(P<0.01)and T2DM(P<0.01)groups.In addition,the difference in the prevalence of"disturbances of pigmentation"was also significant in NGT and T2DM groups(P<0.01).Conclusions:There was a high prevalence of skin diseases in the Da Qing Diabetes Study.To address the skin diseases in the Da Qing Diabetes Study,increased awareness and intervention measures should be implemented.

Psychosomatic skin disease treated by acupuncture and moxibustion

Based on the theory of "regulating body" integrated with "regulating mind",following the principle of "treating the body and mind simultaneously,treating both symptoms and root causes",Prof.Shao-jun WANG adopts flexibly the different techniques,including body acupuncture,wrist-ankle acupuncture,umbilical acupuncture,fire needling,moxibustion,and cupping,etc.for the treatment of psychosomatic skin diseases.According to the manifestations of skin diseases in different stages,she adopts different acupuncture and moxibustion techniques for the comprehensive treatment of psychosomatic skin diseases,including senile pruritus,alopecia areata,neurodermatitis,eczema,urticaria,and acne,on the basis of syndrome differentiation and different acupoint selections,and achieves good results.

Global output of research on epidermal parasitic skin diseases from 1967 to 2017

Background:Epidermal parasitic skin diseases(EPSD)occur in most countries and cause a considerable health and economic burden,particularly in the tropics and subtropics.The aim of this study was to assess and analyse peerreviewed literature on EPSD in humans.The results of this study serve as an indicator of the extent the scientific community,health authorities,and international health agencies interact with EPSD as a health problem that is commonly associated with poverty and poor hygiene.Methods:A bibliometric analysis methodology was used.The Scopus database was used to retrieve documents about EPSD for the study period(1967-2017).The study focused on scabies,tungiasis,pediculosis,hookwormrelated cutaneous larva migrans(HrCLM),myiasis,and cutaneous strongyloidiasis.Documents that specifically and explicitly discuss EPSD in animals,aquatic organisms,and birds were excluded.Results:In total,4186 documents were retrieved.A fluctuated growth of publications on EPSD in the past five decades was found.The retrieved documents received 43301 citations,an average of 10.3 citations per article and an h-index of 74.The keywords“scabies”and was the most commonly encountered keyword followed by the keywords“head lice”and“pediculosis”.The most active journal involved in publishing articles on EPSD was the International Journal of Dermatology(164;3.9%).Researchers from 93 different countries published the retrieved articles.The USA led with 735(17.6%)documents,followed by the UK(274;6.5%),and Germany(259;6.2%).In terms of institutions,the Charite-Universitätsmedizin Berlin in Germany was the most active in this field with 78(1.9%)publications,followed by the Universidade Federal do Cearáin Brazil with 52(1.2%)publications.Conclusions:Research on scabies and pediculosis dominated the field of EPSD research to the expense of tungiasis,HrCLM,myiasis,and cutaneous strongyloidiasis.There was an underrepresentation of literature from the tropics and subtropics despite EPSD being common in these areas.This could possibly be explained by the presence of limited number of non-English journals in the Scopus database.International research collaborations and research networking should be strengthened to help advance and prioritize research on EPSD.

Antibacterial Activity of Extracts from Zostera marina Against Pathogens of Apostichopus japonicus Skin Ulceration Disease

The purpose of this study was to investigate the antibacterial activity of extracts from Zostera marina against the pathogens of Apostichopus japonicus skin ulceration disease.When 95% ethanol (v/v) solvent was used to extract Zostera marina at 50℃,aqueous extract (ZA) showed obvious bacteriostatic effects on the tested bacterial strains (inhibition halo diameters between 8.23 mm and 13.62 mm),whereas the ethyl acetate extract (ZE) was almost inactive.The minimal inhibitory concentration (MIC) of ZA against four pathogens were homogeneous at 12.8 g L-1.ZA components were analyzed by thin layer chromatography (TLC) assay and six fractions were obtained.In another study,the six fractions showed inhibitory effects against the tested bacteria while their functions seemed to counteract the ZA activity.

Study on Preventive Effect of Cecropin and Lysozyme on Skin Fester Disease in Trionyx sinensis

[ Objective ] This study was to investigate the preventive effect of cecropin and lysozyme on skin fester disease in Trionyx sinensis. [ Method ] By employing intramuscular injection method, the preventive effect of mixtures of cecropin （10 mg/kg） and lysozyme（ 10 mg/kg） and of cecropin（6 mg/kg） and lysozyme（ 5 mg/kg） were challenged on Aeromonas encheleia-caused skin fester disease in T. sinensis. The mortality and relative percent survival were used evaluate the preventive effect of different drugs. The serum agglutination titer, cytophagocytosis and bactericidal activity were detected to compare the effect of drugs on the immunity and disease resistance of T. sinensis. [ Result] The residue concentrations of gentamicin and lysozyme in serum were compared to evaluate the side effect of drugs used challenged. The results showed that combined use of cecropin and lysozyme gave the best preventive effect and immunity in T. sinensis, with an extremely significant difference from other treatments （ P 〈 0.01 ）. Analysis of drug residue in serum indicated that lysozyme was completely metabolized 6 days after medication, while gentamicin residue was still observed 12 days later. [ Conclusion] The combined application of cecropin and lysozyme showed better preventive effect than antibiotics in fester disease of T. sinensis. Moreover, no drug residue and side effect were observed in the combined use treatment. This method avoids the side effect of antibiotics, worthy of extension at commercial scale.

Scalable Skin Lesion Multi-Classification Recognition System

Skin lesion recognition is an important challenge in the medical field.In this paper,we have implemented an intelligent classification system based on convolutional neural network.First of all,this system can classify whether the input image is a dermascopic image with an accuracy of 99%.And then diagnose the dermoscopic image and the non-skin mirror image separately.Due to the limitation of the data,we can only realize the recognition of vitiligo by non-skin mirror.We propose a vitiligo recognition based on the probability average of three structurally identical CNN models.The method is more efficient and robust than the traditional RGB color space-based image recognition method.For the dermoscopic classification model,we were able to classify 7 skin lesions,use weighted optimization to overcome the unbalanced data set,and greatly improve the sensitivity of the model by means of model fusion.The optimization and expansion of the system depend on the increase of database.

A Lightweight Approach for Skin Lesion Detection Through Optimal Features Fusion

Skin diseases effectively influence all parts of life.Early and accurate detection of skin cancer is necessary to avoid significant loss.The manual detection of skin diseases by dermatologists leads to misclassification due to the same intensity and color levels.Therefore,an automated system to identify these skin diseases is required.Few studies on skin disease classification using different techniques have been found.However,previous techniques failed to identify multi-class skin disease images due to their similar appearance.In the proposed study,a computer-aided framework for automatic skin disease detection is presented.In the proposed research,we collected and normalized the datasets from two databases(ISIC archive,Mendeley)based on six Basal Cell Carcinoma(BCC),Actinic Keratosis(AK),Seborrheic Keratosis(SK),Nevus(N),Squamous Cell Carcinoma(SCC),and Melanoma(M)common skin diseases.Besides,segmentation is performed using deep Convolutional Neural Networks(CNN).Furthermore,three types of features are extracted from segmented skin lesions:ABCD rule,GLCM,and in-depth features.AlexNet transfer learning is used for deep feature extraction,while a support vector machine(SVM)is used for classification.Experimental results show that SVM outperformed other studies in terms of accuracy,as AK disease achieved 100%accuracy,BCC 92.7%,M 95.1%,N 97.8%,SK 93.1%,SCC 91.4%with a global accuracy of 95.4%.

Skin lesion classification system using a Knearest neighbor algorithm

One of the most critical steps in medical health is the proper diagnosis of the disease.Dermatology is one of the most volatile and challenging fields in terms of diagnosis.Dermatologists often require further testing,review of the patient’s history,and other data to ensure a proper diagnosis.Therefore,finding a method that can guarantee a proper trusted diagnosis quickly is essential.Several approaches have been developed over the years to facilitate the diagnosis based on machine learning.However,the developed systems lack certain properties,such as high accuracy.This study proposes a system developed in MATLAB that can identify skin lesions and classify them as normal or benign.The classification process is effectuated by implementing the K-nearest neighbor(KNN)approach to differentiate between normal skin and malignant skin lesions that imply pathology.KNN is used because it is time efficient and promises highly accurate results.The accuracy of the system reached 98%in classifying skin lesions.