Deep Learning-Based Stacked Auto-Encoder with Dynamic Differential Annealed Optimization for Skin Lesion Diagnosis

Intelligent diagnosis approaches with shallow architectural models play an essential role in healthcare.Deep Learning(DL)models with unsupervised learning concepts have been proposed because high-quality feature extraction and adequate labelled details significantly influence shallow models.On the other hand,skin lesionbased segregation and disintegration procedures play an essential role in earlier skin cancer detection.However,artefacts,an unclear boundary,poor contrast,and different lesion sizes make detection difficult.To address the issues in skin lesion diagnosis,this study creates the UDLS-DDOA model,an intelligent Unsupervised Deep Learning-based Stacked Auto-encoder(UDLS)optimized by Dynamic Differential Annealed Optimization(DDOA).Pre-processing,segregation,feature removal or separation,and disintegration are part of the proposed skin lesion diagnosis model.Pre-processing of skin lesion images occurs at the initial level for noise removal in the image using the Top hat filter and painting methodology.Following that,a Fuzzy C-Means(FCM)segregation procedure is performed using a Quasi-Oppositional Elephant Herd Optimization(QOEHO)algorithm.Besides,a novel feature extraction technique using the UDLS technique is applied where the parameter tuning takes place using DDOA.In the end,the disintegration procedure would be accomplished using a SoftMax(SM)classifier.The UDLS-DDOA model is tested against the International Skin Imaging Collaboration(ISIC)dataset,and the experimental results are examined using various computational attributes.The simulation results demonstrated that the UDLS-DDOA model outperformed the compared methods significantly.

Optimal Artificial Intelligence Based Automated Skin Lesion Detection and Classification Model

Skin lesions have become a critical illness worldwide,and the earlier identification of skin lesions using dermoscopic images can raise the survival rate.Classification of the skin lesion from those dermoscopic images will be a tedious task.The accuracy of the classification of skin lesions is improved by the use of deep learning models.Recently,convolutional neural networks(CNN)have been established in this domain,and their techniques are extremely established for feature extraction,leading to enhanced classification.With this motivation,this study focuses on the design of artificial intelligence(AI)based solutions,particularly deep learning(DL)algorithms,to distinguish malignant skin lesions from benign lesions in dermoscopic images.This study presents an automated skin lesion detection and classification technique utilizing optimized stacked sparse autoen-coder(OSSAE)based feature extractor with backpropagation neural network(BPNN),named the OSSAE-BPNN technique.The proposed technique contains a multi-level thresholding based segmentation technique for detecting the affected lesion region.In addition,the OSSAE based feature extractor and BPNN based classifier are employed for skin lesion diagnosis.Moreover,the parameter tuning of the SSAE model is carried out by the use of sea gull optimization(SGO)algo-rithm.To showcase the enhanced outcomes of the OSSAE-BPNN model,a comprehensive experimental analysis is performed on the benchmark dataset.The experimentalfindings demonstrated that the OSSAE-BPNN approach outper-formed other current strategies in terms of several assessment metrics.

Automated Skin Lesion Diagnosis and Classification Using Learning Algorithms

Due to the rising occurrence of skin cancer and inadequate clinical expertise,it is needed to design Artificial Intelligence(AI)based tools to diagnose skin cancer at an earlier stage.Since massive skin lesion datasets have existed in the literature,the AI-based Deep Learning(DL)modelsfind useful to differentiate benign and malignant skin lesions using dermoscopic images.This study develops an Automated Seeded Growing Segmentation with Optimal EfficientNet(ARGS-OEN)technique for skin lesion segmentation and classification.The proposed ASRGS-OEN technique involves the design of an optimal EfficientNet model in which the hyper-parameter tuning process takes place using the Flower Pollination Algorithm(FPA).In addition,Multiwheel Attention Memory Network Encoder(MWAMNE)based classification technique is employed for identifying the appropriate class labels of the dermoscopic images.A comprehensive simulation analysis of the ASRGS-OEN technique takes place and the results are inspected under several dimensions.The simulation results highlighted the supremacy of the ASRGS-OEN technique on the applied dermoscopic images compared to the recently developed approaches.

Skin Lesion Classification System Using Shearlets

The main cause of skin cancer is the ultraviolet radiation of the sun.It spreads quickly to other body parts.Thus,early diagnosis is required to decrease the mortality rate due to skin cancer.In this study,an automatic system for Skin Lesion Classification(SLC)using Non-Subsampled Shearlet Transform(NSST)based energy features and Support Vector Machine(SVM)classifier is proposed.Atfirst,the NSST is used for the decomposition of input skin lesion images with different directions like 2,4,8 and 16.From the NSST’s sub-bands,energy fea-tures are extracted and stored in the feature database for training.SVM classifier is used for the classification of skin lesion images.The dermoscopic skin images are obtained from PH^(2) database which comprises of 200 dermoscopic color images with melanocytic lesions.The performances of the SLC system are evaluated using the confusion matrix and Receiver Operating Characteristic(ROC)curves.The SLC system achieves 96%classification accuracy using NSST’s energy fea-tures obtained from 3^(rd) level with 8-directions.

A Saliency Based Image Fusion Framework for Skin Lesion Segmentation and Classification

Melanoma,due to its higher mortality rate,is considered as one of the most pernicious types of skin cancers,mostly affecting the white populations.It has been reported a number of times and is now widely accepted,that early detection of melanoma increases the chances of the subject’s survival.Computer-aided diagnostic systems help the experts in diagnosing the skin lesion at earlier stages using machine learning techniques.In thiswork,we propose a framework that accurately segments,and later classifies,the lesion using improved image segmentation and fusion methods.The proposed technique takes an image and passes it through two methods simultaneously;one is the weighted visual saliency-based method,and the second is improved HDCT based saliency estimation.The resultant image maps are later fused using the proposed image fusion technique to generate a localized lesion region.The resultant binary image is later mapped back to the RGB image and fed into the Inception-ResNet-V2 pre-trained model-trained by applying transfer learning.The simulation results show improved performance compared to several existing methods.

Statistical Histogram Decision Based Contrast Categorization of Skin Lesion Datasets Dermoscopic Images

Most of the melanoma cases of skin cancer are the life-threatening form of cancer.It is prevalent among the Caucasian group of people due to their light skin tone.Melanoma is the second most common cancer that hits the age group of 15–29 years.The high number of cases has increased the importance of automated systems for diagnosing.The diagnosis should be fast and accurate for the early treatment of melanoma.It should remove the need for biopsies and provide stable diagnostic results.Automation requires large quantities of images.Skin lesion datasets contain various kinds of dermoscopic images for the detection of melanoma.Three publicly available benchmark skin lesion datasets,ISIC 2017,ISBI 2016,and PH2,are used for the experiments.Currently,the ISIC archive and PH2 are the most challenging and demanding dermoscopic datasets.These datasets’pre-analysis is necessary to overcome contrast variations,under or over segmented images boundary extraction,and accurate skin lesion classification.In this paper,we proposed the statistical histogram-based method for the pre-categorization of skin lesion datasets.The image histogram properties are utilized to check the image contrast variations and categorized these images into high and low contrast images.The two performance measures,processing time and efficiency,are computed for evaluation of the proposed method.Our results showed that the proposed methodology improves the pre-processing efficiency of 77%of ISIC 2017,67%of ISBI 2016,and 92.5%of PH2 datasets.

Computer Vision with Machine Learning Enabled Skin Lesion Classification Model

Recently,computer vision(CV)based disease diagnosis models have been utilized in various areas of healthcare.At the same time,deep learning(DL)and machine learning(ML)models play a vital role in the healthcare sector for the effectual recognition of diseases using medical imaging tools.This study develops a novel computer vision with optimal machine learning enabled skin lesion detection and classification(CVOML-SLDC)model.The goal of the CVOML-SLDC model is to determine the appropriate class labels for the test dermoscopic images.Primarily,the CVOML-SLDC model derives a gaussian filtering(GF)approach to pre-process the input images and graph cut segmentation is applied.Besides,firefly algorithm(FFA)with EfficientNet based feature extraction module is applied for effectual derivation of feature vectors.Moreover,naïve bayes(NB)classifier is utilized for the skin lesion detection and classification model.The application of FFA helps to effectually adjust the hyperparameter values of the EfficientNet model.The experimental analysis of the CVOML-SLDC model is performed using benchmark skin lesion dataset.The detailed comparative study of the CVOML-SLDC model reported the improved outcomes over the recent approaches with maximum accuracy of 94.83%.

Optimized Convolutional Neural Network Models for Skin Lesion Classification

Skin cancer is one of themost severe diseases,andmedical imaging is among themain tools for cancer diagnosis.The images provide information on the evolutionary stage,size,and location of tumor lesions.This paper focuses on the classification of skin lesion images considering a framework of four experiments to analyze the classification performance of Convolutional Neural Networks(CNNs)in distinguishing different skin lesions.The CNNs are based on transfer learning,taking advantage of ImageNet weights.Accordingly,in each experiment,different workflow stages are tested,including data augmentation and fine-tuning optimization.Three CNN models based on DenseNet-201,Inception-ResNet-V2,and Inception-V3 are proposed and compared using the HAM10000 dataset.The results obtained by the three models demonstrate accuracies of 98%,97%,and 96%,respectively.Finally,the best model is tested on the ISIC 2019 dataset showing an accuracy of 93%.The proposed methodology using CNN represents a helpful tool to accurately diagnose skin cancer disease.

Deep Learning with Backtracking Search Optimization Based Skin Lesion Diagnosis Model

Nowadays,quality improvement and increased accessibility to patient data,at a reasonable cost,are highly challenging tasks in healthcare sector.Internet of Things(IoT)and Cloud Computing(CC)architectures are utilized in the development of smart healthcare systems.These entities can support real-time applications by exploiting massive volumes of data,produced by wearable sensor devices.The advent of evolutionary computation algorithms andDeep Learning(DL)models has gained significant attention in healthcare diagnosis,especially in decision making process.Skin cancer is the deadliest disease which affects people across the globe.Automatic skin lesion classification model has a highly important application due to its fine-grained variability in the presence of skin lesions.The current research article presents a new skin lesion diagnosis model i.e.,Deep Learning with Evolutionary Algorithm based Image Segmentation(DL-EAIS)for IoT and cloud-based smart healthcare environments.Primarily,the dermoscopic images are captured using IoT devices,which are then transmitted to cloud servers for further diagnosis.Besides,Backtracking Search optimization Algorithm(BSA)with Entropy-Based Thresholding(EBT)i.e.,BSA-EBT technique is applied in image segmentation.Followed by,Shallow Convolutional Neural Network(SCNN)model is utilized as a feature extractor.In addition,Deep-Kernel Extreme LearningMachine(D-KELM)model is employed as a classification model to determine the class labels of dermoscopic images.An extensive set of simulations was conducted to validate the performance of the presented method using benchmark dataset.The experimental outcome infers that the proposed model demonstrated optimal performance over the compared techniques under diverse measures.

Classification and Research of Skin Lesions Based on Machine Learning

Classification of skin lesions is a complex identification challenge.Due to the wide variety of skin lesions,doctors need to spend a lot of time and effort to judge the lesion image which zoomed through the dermatoscopy.The diagnosis which the algorithm of identifying pathological images assists doctors gets more and more attention.With the development of deep learning,the field of image recognition has made long-term progress.The effect of recognizing images through convolutional neural network models is better than traditional image recognition technology.In this work,we try to classify seven kinds of lesion images by various models and methods of deep learning,common models of convolutional neural network in the field of image classification include ResNet,DenseNet and SENet,etc.We use a fine-tuning model with a multi-layer perceptron,by training the skin lesion model,in the validation set and test set we use data expansion based on multiple cropping,and use five models’ensemble as the final results.The experimental results show that the program has good results in improving the sensitivity of skin lesion diagnosis.

Multicentric reticulohistiocytosis with prominent skin lesions and arthritis:A case report

BACKGROUND Multicentric reticulohistiocytosis(MRH)is a rare non-Langerhans histiocytosis of unknown etiology characterized by papulonodular skin lesions and progressive,erosive arthritis.To date,there have been approximately 300 cases of MRH reported worldwide.The majority of patients are Caucasian from western countries,and Asian patients are rare.Here,we report a case of MRH in a Chinese patient.CASE SUMMARY A 38-year-old male was admitted to the hospital with a rash that had persisted for over 2 years and bilateral knee pain for over 1 year.The patient’s symptoms had previously been misdiagnosed as eczema when there were only skin symptoms and was finally diagnosed as MRH after a skin biopsy of the left upper back.The patient was treated with glucocorticoids combined with an immunosuppressive regimen.While the skin lesions on both arms,abdomen,and back subsided,the skin lesions on the rest of the body did not increase.The interphalangeal joints of both thumbs and bilateral knee joints remained swollen and painful.CONCLUSION The case will help clinicians better identify and treat this disease in the absence of epidemiological studies or randomized controlled data.

Effect of Zhiyang Pingfu Liquid on epidermal growth factor receptor inhibitor-related skin lesion

Objective:To verify the efficacy of the Chinese medicine“Zhiyang Pingfu Liquid”on the lesion associated with Epidermal Growth Factor Receptor Inhibitors(EGFRIs).Methods:Female BN rats were divided into Control group and Gefitinib group randomly.The Gefitinib group was administered gefitinib for 21 days.After 21 days,the rats in the Gefitinib group were grouped again and randomly divided into Model group,Gefitinib+ZY group,and Gefitinib+NS group.Starting from day 22,rats in Gefitinib+ZY or NS were given different drugs for 7 days besides the other conditions are as the same as before.Observe the morphological changes and histopathological changes of the skin during the research.The changes of inflammatory factors such as TNF-αand IL-6 in the serum of were detected by ELISA.Results:The application of“Zhiyang Pingfu Liquid”for 7 days could significantly reduce the skin inflammation whether in gross or pathological view.The concentration of TNF-αand IL-6 in Gefitinib+ZY is significantly lower than those in the Model group(P=0.002,P=0.002)and there is no significant changes compared with the Control group(P=0.279,P=0.165).Conclusion:Chinese herbal“Zhiyang Pingfu Liquid”can reduce the lesion and inflammatory caused by EGFRIs.

Differential Diagnosis of Post-Procedural Skin Lesions: A Case Report

Skin reactions caused by interventional pain procedures are well documented in literature, ranging from fistula formation to urticarial allergic reactions and infections. Burn lesions may also occur, however far less common;and as pain physician we must be cognizant of this possible complication and its etiologies. This is difficult in an outpatient setting where a patient cannot be regularly monitored, their adherence to prescribed therapies is unclear, and reporting is often done via phone, ancillary staff, and outside facility records. These compounding factors require clinicians to consider a broad differential and be comfortable with instituting myriad therapies or appropriately involve outside consultation for thorough patient care.

Protective Roles of Some Leafy and Non-leafy Vegetables against the Severity of Arsenic-induced Skin Lesions among Women Living in Rural Bangladesh:A Case Control Study

Arsenicosis is common among villagers as they drink more contaminated-water since the arsenic-crisis in Bangladesh.Supplementation of vitamins and micronutrients in counteracting arsenic toxicity has been proved for arsenic treatment.This study was intended to assess protective and beneficial roles of some commonly eaten vegetables on the development and severity of arsenic-induced skin lesions.A case-control study among(N=122)adult rural-women(62 cases had various forms of arsenical skin-lesions e.g.melanosis/keratosis/mixed-lesions and 60 sex-age-matched healthy-controls)was conducted in Shaharstee Upazilla of Chandpur district,Bangladesh.Socio-demographic data recorded in a pre-tested-questionnaire,‘per-day vegetables ingestion’of cases and controls were measured qualitative and quantitatively(24-hour recall-methods,food-frequency/week and food history-record/week).Multiple logistic regression/MLR analyses were performed to find out protective roles of some dietary leafy-vegetables/LVs and non-leafy vegetables/NLVs on arsenicosis and their influences on the degree of severity of arsenicosis also determined.Abstinence from taking some LVs/NLVs among cases than controls is associated with increased risk for arsenicosis(P<0.05).Amongst all most-frequently eaten vegetables(n=17)per day Momordica diocia has the highest skin protective role on arsenicosis[Adjusted odds ratio/AOR 8.2,95%CI(2.11-31.9),P=<0.01],followed by Ipomoea acquatica(AOR:7.3),Basella alba(AOR:6.2),Solanum tuberosum(AOR:4.0),Vigna unguiculata sesquipedalis(AOR:3.2),Trichosanthes anguina(AOR:1.2)and Abelmoschus esculentus(AOR:1.2).Moreover,severe skin lesion was observed as compared to non-severe cases(mild/moderate)for less intake frequencies of vegetables.This study outlined that commonly eaten vegetables have protective and beneficial roles on arsenic-induced skin lesions.Large samples longitudinal study of this important field of therapeutic-intervention is warranted.

CD13/APN expression in peripheral blood lymphocytes and skin lesions in patients with advanced psoriasis vulgaris

Objective: To observe the expression of CD13/APN in peripheral blood lymphocytes and skin lesions of patients with advanced psoriasis vulgaris, and discuss its effect on the pathogenesis of psoriasis. Methods: CD13 expression in peripheral blood lymphocytes and skin lesions was detected by flow cytometry and immunohistochemical technique, respectively. Results were compared with those of healthy controls. Results: CD13 expression was significantly higher in peripheral blood lymphocytes of patients with advanced psoriasis vulgaris than in that of healthy controls, and in skin lesions than in healthy skin tissues. The expression was mainly in the suprabasal layers of skin lesions, and positively correlated to PASI (R=0.78029). Conclusion: The significantly higher expression of CD13 in peripheral blood lymphocytes and skin lesions of the patients with advanced psoriasis vulgaris probably is related to immunological abnormality, blood vessel abnormality and proliferation of keratinocyte in the pathogenic course of psoriasis. It may be a novel and effective way to treat psoriasis with specific CD13 inhibitors.

Effect of Image Noise on the Classification of Skin Lesions Using Deep Convolutional Neural Networks

Skin lesions are in a category of disease that is both common in humans and a major cause of death.The classification accuracy of skin lesions is a crucial determinant of the success rate of curing lethal diseases.Deep Convolutional Neural Networks(CNNs)are now the most prevalent computer algorithms for the purpose of disease classification.As with all algorithms,CNNs are sensitive to noise from imaging devices,which often contaminates the quality of the images that are fed into them.In this paper,a deep CNN(Inception-v3)is used to study the effect of image noise on the classification of skin lesions.Gaussian noise,impulse noise,and noise made up of a compound of the two are added to an image dataset,namely the Dermofit Image Library from the University of Edinburgh.Evaluations,based on t-distributed Stochastic Neighbor Embedding(t-SNE)visualization,Receiver Operating Characteristic(ROC)analysis,and saliency maps,demonstrate the reliability of the Inception-v3 deep CNN in classifying noisy skin lesion images.

Smart MobiNet:A Deep Learning Approach for Accurate Skin Cancer Diagnosis

The early detection of skin cancer,particularly melanoma,presents a substantial risk to human health.This study aims to examine the necessity of implementing efficient early detection systems through the utilization of deep learning techniques.Nevertheless,the existing methods exhibit certain constraints in terms of accessibility,diagnostic precision,data availability,and scalability.To address these obstacles,we put out a lightweight model known as Smart MobiNet,which is derived from MobileNet and incorporates additional distinctive attributes.The model utilizes a multi-scale feature extraction methodology by using various convolutional layers.The ISIC 2019 dataset,sourced from the International Skin Imaging Collaboration,is employed in this study.Traditional data augmentation approaches are implemented to address the issue of model overfitting.In this study,we conduct experiments to evaluate and compare the performance of three different models,namely CNN,MobileNet,and Smart MobiNet,in the task of skin cancer detection.The findings of our study indicate that the proposed model outperforms other architectures,achieving an accuracy of 0.89.Furthermore,the model exhibits balanced precision,sensitivity,and F1 scores,all measuring at 0.90.This model serves as a vital instrument that assists clinicians efficiently and precisely detecting skin cancer.

犬利什曼病诊断的回顾性分析

为了总结分析北京地区犬利什曼病的流行特点、临床特征和诊断方法等,本调查收集在中国农业大学教学动物医院确诊的5例利什曼病患犬的病例资料,包括患犬的基本信息、病史、临床症状、病原学检查、实验室检查和影像学检查结果,并对患犬转归情况进行电话追踪访问后汇总。结果显示,3例(3/5)患犬来自北京市门头沟区。所有患犬(5/5)均有不同的皮肤病症状,4例(4/5)患犬体表淋巴结增大;个别患犬症状还包括葡萄膜炎、眼睑炎和不同程度的消瘦等。病原学检查结果显示,所有患犬均呈利氏曼原虫阳性;实验室检查结果显示,患犬表现贫血、高球蛋白血症和肾损伤等;影像学检查结果显示,部分患犬出现肾皮质回声增强。电话回访后得知,4例(4/5)患犬预后良好,1例患犬死亡。结果表明,利什曼病患犬常见临床症状为皮肤病变合并体表淋巴结肿大,但具体临床表现形式多样。通过对皮肤病变组织或淋巴结穿刺物进行利什曼原虫形态学观察和/或PCR特异性核酸检测可确诊该病。利氏曼病为人兽共患病,本调查通过描述患犬不同临床症状表现和诊治方法,为北京市门头沟及其周边区域利氏曼病的诊治提供科学依据,以期提高疾病的诊治效率,保障伴侣动物和人类的健康。

可溶性肿瘤坏死因子样凋亡弱诱导因子在斑秃病人血清及皮损中的表达及临床意义

目的检测可溶性肿瘤坏死因子样凋亡弱诱导因子(sTWEAK)在斑秃病人血清及皮损中的表达水平,并探讨其在斑秃发病过程中的作用及临床意义。方法以2017年10月至2018年11月在首都医科大学附属北京友谊医院确诊并治疗的斑秃病人70例为斑秃组,依据病情分为轻症组(44例)、重症组(26例)。同期该院因色素痣或皮肤良性肿瘤进行头皮手术的人群65例作为对照组。比较各组sTWEAK及相关细胞因子白细胞介素(IL)-4、IL-12、γ干扰素(IFN-γ)蛋白及mRNA表达水平;采用Pearson法分析两指标间相关性。多元线性回归分析各指标与sTWEAK或脱发严重度工具(SALT)评分的关系。结果斑秃组血清及皮损中sTWEAK表达水平[(196.73±32.48)ng/L,(2.35±0.62)ng/L]高于对照组[(121.75±19.62)ng/L,(1.49±0.45)ng/L](P<0.05)。斑秃组血清及皮损中细胞因子IL-12、IFN-γ表达水平高于对照组(P<0.05),IL-4表达水平比较差异无统计学意义(P>0.05)。斑秃病人血清及皮损中sTWEAK表达水平与IL-12及IFN-γ呈正相关,与IL-4表达水平无明显相关性。轻症组血清及皮损中sTWEAK[(173.25±26.84)ng/L,(2.04±0.57)ng/L]、IL-12及IFN-γ表达水平低于重症组[(236.47±42.02)ng/L,(2.87±0.70)ng/L](P<0.05);轻症组、重症组血清及皮损中IL-4表达水平差异无统计学意义(P>0.05);斑秃病人血清、皮损处sTWEAK、IL-12、IFN-γ与SALT评分均呈正相关(P<0.05)。多元线性回归分析显示,IL-12、IFN-γ与血清和皮损处sTWEAK呈显著正相关;血清及皮损处sTWEAK、IL-12、IFN-γ与SALT评分存在正相关性(P<0.05)。结论斑秃病人血清及皮损中sTWEAK表达水平增加,与IL-12及IFN-γ呈正相关,sTWEAK、IL-12及IFN-γ均与斑秃病情严重程度密切相关,sTWEAK可能参与斑秃炎症反应过程。