Intelligent Multiclass Skin Cancer Detection Using Convolution Neural Networks

The worldwide mortality rate due to cancer is second only to cardiovascular diseases.The discovery of image processing,latest artificial intelligence techniques,and upcoming algorithms can be used to effectively diagnose and prognose cancer faster and reduce the mortality rate.Efficiently applying these latest techniques has increased the survival chances during recent years.The research community is making significant continuous progress in developing automated tools to assist dermatologists in decision making.The datasets used for the experimentation and analysis are ISBI 2016,ISBI 2017,and HAM 10000.In this work pertained models are used to extract the efficient feature.The pertained models applied are ResNet,InceptionV3,and classical feature extraction techniques.Before that,efficient preprocessing is conducted on dermoscopic images by applying various data augmentation techniques.Further,for classification,convolution neural networks were implemented.To classify dermoscopic images on HAM 1000 Dataset,the maximum attained accuracy is 89.30%for the proposed technique.The other parameters for measuring the performance attained 87.34%(Sen),86.33%(Pre),88.44%(F1-S),and 11.30%false-negative rate(FNR).The class with the highest TP rate is 97.6%for Melanoma;whereas,the lowest TP rate was for the Dermatofibroma class.For dataset ISBI2016,the accuracy achieved is 97.0%with the proposed classifier,whereas the other parameters for validation are 96.12%(Sen),97.01%(Pre),96.3%(F1-S),and further 3.7%(FNR).For the experiment with the ISBI2017 dataset,Sen,Pre,F1-S,and FNR were 93.9%,94.9%,93.9%,and 5.2%,respectively.

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.

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 Usefulness of the Artificial Intelligence Data in Analyzing the Skin in the Era of the Fourth Industrial Revolution

In the World economy forum Global Challenge Insight Report titled “The Future of Jobs-Employment, Skills and Workforce Strategy for the Fourth Industrial Revolution (FIR) in 2016”, a new industrial revolution was predicted to occur in the near future. This is followed by the opinion that it would be mandatory to prepare for the FIR because it will definitely change people’s way of working, consuming and thinking. There is a controversy as to the potential of AI in health care. To date, however, remarkable achievements have been made in the field of medicine, particularly entailing dermatology. Therefore, this study explored the usefulness of the AI data in analyzing the skin in the era of the FIR. The current study finally included a total of 300 subjects, for whom a self-reporting questionnaire survey was performed between June 09 and July 18, 2020. The current study proposed the following hypothesis: The AI data might be useful in analyzing the skin in the era of the FIR. This hypothesis was accepted. In conclusion, the current study suggests that the AI data might be useful in analyzing the skin in the era of the FIR. But this deserves further study.

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.

Development of Novel Magnetic Responsive Intelligent Fluid, Hybrid Fluid (HF), for Production of Soft and Tactile Rubber

For the purpose of the replacement of Magnetic Fluid (MF) which is effective in the production of an artificial soft and tactile skin for the robot, etc. by utilizing a rubber solidification method with electrolytic polymerization, we proposed a novel magnetic responsive intelligent fluid, Hybrid Fluid (HF). HF is structured with water, kerosene, silicon oil having Polydimethylsiloxane (PDMS) and Polyvinyl Alcohol (PVA) as well as magnetic particles and surfactant. The state of HF changes as jelly or fluid by their rates of the constituents and motion style. In the present paper, we presented the characteristics of HF: the viscosity and the magnetization are respectively equivalent to those of other magnetic responsive fluids, MF and their solvents. For the structure, HF is soluble simultaneously with both diene and non-diene rubbers. The diene rubber such as Natural Rubber (NR) or Chloroprene (CR) has a role in the feasibility of electrolytic polymerization and the non-diene rubber such as silicon oil rubber (Q) has a role in defense against deterioration. Therefore, the electrolytically polymerized HF rubber by mixing NR, CR as well as Q is effective for the artificial soft and tactile skin. It is responsive to pressure and has optimal property on piezoelectricity in the case of the mixture of Ni particles as filler. HF is effective in the production of the artificial soft and tactile skin made of rubber.

人形机器人传感器发展建议与对策研究

人形机器人集成人工智能、高端制造、新材料等先进技术,有望成为继计算机、智能手机、新能源汽车后又一颠覆性、划时代的科技产品,发展潜力大,应用前景广。智能传感器作为人形机器人感知系统的核心组件,是实现人形机器人智能感知和自主操作的关键,也是迈向自主化、智能化的基础。高质量发展人形机器人智能传感器,有助于赋能人形机器人产业高质量发展和培育发展新质生产力,推进新型工业化。

智媒时代建筑表皮的媒介化研究

智媒时代背景下,建筑表皮的媒介化带来了城市空间设计语言的转向。探讨了建筑表皮如何建构起城市空间的丰富性、交互性,以及如何作为营造虚拟数字化城市空间的入口。

基于皮肤影像的人工智能研究新进展

近年来,基于皮肤影像的人工智能(artificial intelligence,AI)研究取得较大进展,已有许多研究通过使用皮肤病影像数据建立深度学习(deep learning,DL)模型,实现了自动诊断和鉴别皮肤疾病的功能。除了常见的皮肤病和皮肤肿瘤,AI还能分析皮肤美容相关特征,为个性化皮肤美容治疗方案提供指导。尽管AI辅助皮肤影像学诊断的性能仍有待提升,但随着技术的成熟和发展,其应用前景将更广阔。

面向电子皮肤的智能材料构建策略

电子皮肤作为具有模仿人类皮肤感知功能的新型的柔性可穿戴传感器,具有轻薄、柔软、灵活等特点,可将外界刺激转化为不同的输出信号,近年来在健康监测、人机交互等领域展现出巨大的应用潜力。本文从构建电子皮肤的智能材料角度出发,对电子皮肤常用基体和导电填料及其几何结构构建等方面进行了综述,并基于电子皮肤应用所需面对的复杂环境对其生物相容性、黏附性、自修复性、自供电性等应用性能需求进行讨论,进而指出电子皮肤在研究过程中仍然存在对人体皮肤的综合感知性能差、制备工艺复杂且昂贵、感知刺激信号存在滞后性等问题,通过材料和结构优化提升电子皮肤基础性能,从而构建优异性能、多功能化、多种外界刺激同步检测成为电子皮肤发展趋势,并且在医疗诊断、软体机器人、智能假肢和人机交互等领域表现出极大的潜力。

皮肤超声:2023年度研究进展与未来挑战

在过去的2023年中,高频超声技术在皮肤疾病领域的应用又获得了长足发展,不仅在基础研究方面取得了前沿成果,也在临床一线应用中发挥了更为重要的作用。具体而言,在皮肤疾病的诊断、治疗前后评估、人工智能诊断、基础研究成果转化以及新型肿瘤治疗方法等领域,高频超声技术为皮肤科医师提供了更多维度和更高精度的临床诊疗信息和干预手段。本文对2023年皮肤超声技术的研究热点和最新进展进行全面的回顾和总结。

人工智能在基层全科医生实践中的应用:基于皮肤病诊断与病程管理的视角

背景基层全科医生在皮肤病诊断和管理方面面临挑战,凸显了对人工智能(AI)辅助系统的迫切需求。AI技术在提高诊疗效率中具有潜力,但目前针对其在基层医疗实践中的应用研究相对有限。目的探讨AI辅助系统在基层全科医生皮肤病诊断与病程管理中的应用效果。方法于2022年12月—2024年3月,在杭州市社区卫生服务中心招募自愿参与研究的全科医生19名,采用随机数字表法,将其分为AI组10名、对照组9名;选取该时期两组医生接诊的皮肤病患者90例,AI组50例、对照组40例。AI组医生使用睿肤AI辅助系统进行皮肤病的诊断和病程管理,对照组医生不使用AI系统、按常规流程诊治,两组医生在接诊过程中均收集了患者的病历、实验室检查结果和皮损照片。由2名皮肤病专家远程会诊,评估两组医生的诊断准确性。分别于接诊的第1、14天对患者进行皮肤病生活质量指数(DLQI)评分,对两组患者进行满意度测评,对AI组全科医生进行睿肤AI辅助系统使用体验测评。结果AI组和对照组患者的性别、年龄、学历比较,差异无统计学意义(P>0.05);两组医生的性别、年龄、学历、职称比较,差异无统计学意义(P>0.05)。AI组全科医生的皮肤病诊断准确率高于对照组(64.0%vs 37.5%,P=0.012)。治疗14 d后,AI组、对照组患者的DLQI评分较治疗前均有改善(P<0.05),AI组改善程度优于对照组(P<0.05)。AI组患者的满意度高于对照组(P=0.024),AI组患者第14天DLQI评分与患者满意度呈正相关(r_(s)=0.471,95%CI=0.186~0.683,P=0.002),DLQI评分的改善程度与患者满意度亦呈正相关(r_(s)=0.816,95%CI=0.676~0.899,P<0.001)。问卷调查结果显示,大多数医生对AI辅助系统的使用体验持积极态度,认为其在诊断选择(70.0%)、辅助诊断(80.0%)、治疗建议(60.0%)和专业知识提供方面(90.0%)具有实际价值,90.0%的医生表示会继续使用AI辅助系统。结论在基层医疗环境中应用AI辅助系统可以提升全科医生的皮肤病诊断准确率,改善患者的生活质量和就诊满意度,且大多数医生对AI辅助系统的使用体验持积极态度。

拥抱人工智能技术,开拓皮肤病治疗新态势

随着科学技术的不断升级,人工智能(artifical intelligence,AI)已然已经成为当下各行各业产业及技术升级的新的发动机。中医是我国的瑰宝,皮肤又是人体最大的器官,中医皮肤科近年来各大流派医疗技术蓬勃发展,如何将中医皮肤疾病的研究与AI技术相结合,高效的提升临床及科研能力和成果产出率,已经成为中医皮肤科医疗专家与AI专业人员非常值得跨界研究的方向。

卷积神经网络人工智能在皮肤病诊断中的应用进展

皮肤病种类繁多,临床诊断和鉴别诊断困难,辅助诊疗手段有限。而近年来兴起的基于深度学习的卷积神经网络人工智能在皮肤病的诊断与鉴别诊断中表现出了与皮肤科医生相似的水平,特别是在皮肤恶性肿瘤、色素障碍性皮肤病、炎症性及感染性皮肤病中均有重要的应用价值和前景。该文主要对卷积神经网络的基本原理及结构、常见模型、卷积神经网络在皮肤病诊断中的应用,以及卷积神经网络的优势和局限性进行综述。未来人工智能将有可能为更多的临床医生提供帮助,解决部分医疗水平欠发达地区看病难问题,为慢性皮肤病患者提供居家护理服务,以及自动跟踪和监测皮肤病等。

飞机蒙皮损伤智能检测方法

本文基于YOLO v5提出了一种飞机蒙皮损伤检测网络,采用styleGAN来生成合成图像,用于训练所提出的飞机蒙皮缺陷检测网络,通过提出的基于StyleGAN的YOLO v5模型,提高了检测小目标、捕获低灵敏度空间信息和进行全局优化的能力。通过现场拍摄和合成的蒙皮损伤照片,对本文开发的模型进行了训练、验证和测试。本文提出的检测模型的准确率和召回率分别达到92.2%和92.3%,分别比原始YOLO v5高10.7%和12.5%。基于StyleGAN的YOLO v5具有高精度和高鲁棒性,该模型可以显著提高飞机蒙皮损伤检测效率,降低误判率。

聚偏氟乙烯/碳纳米管基压电式电子皮肤的制备及应用

聚偏氟乙烯(PVDF)是一种常见的压电材料。本文通过静电纺丝的方法,制备PVDF/碳纳米管(CNT)基电子皮肤,并对电子皮肤压力感知性能进行研究,测试稳定性和耐久性。实验证明:该电子皮肤可应用于智能机器人和可穿戴设备。

平行皮肤:基于视觉的皮肤病分析框架

随着计算机与人工智能的快速发展,基于图像感知的皮肤病分析方法取得一些成果.然而,以深度学习为主的计算机辅助分析方法依赖于领域专家标注的医学大数据,诊断结果缺乏医学可解释性.为此,文中提出基于视觉的皮肤病分析统一框架——平行皮肤.启发于ACP方法与平行医学图像分析框架,通过构建人工皮肤图像系统实现数据选择与生成,通过预测学习的计算实验完成诊断分析模型构建与评估,并利用描述学习与指示学习融合专家知识,引导人工图像系统数据生成与选择,从而实现闭环诊断分析模型优化.

人工智能技术在皮肤病辅助诊断的应用研究

目的:皮肤病种类繁多,采用机器学习、计算机视觉等人工智能技术可实现自动、快速分类及特征识别,分析并研究国内外在皮肤病辅助诊断方面的人工智能技术应用。方法:临床诊断使用的皮肤病图像主要包括皮肤镜图像以及皮肤组织病理图像等,所使用的方法与样本数量及质量直接相关,针对海量皮肤镜图像,采用卷积神经网络迁移学习可实现疾病类型的分类;针对小样本量皮肤镜图像,结合皮肤学诊断原理和机器学习建模实现特定疾病类型诊断;针对有限样本且复杂的皮肤组织病理图像,运用图像分割和机器学习建模实现病理图像自动区域标注。结果:在皮肤病辅助诊断方面,结合人工智能技术,国内外研究机构已经实现肤质特征识别、自动区域标注、疾病分类等应用。结论:随着用于皮肤病诊断的人工智能技术发展,有望大幅提高接诊效率,为医生提供客观的辅助支撑,减轻负担。

应用于智能轮椅控制的头部姿态识别

针对适用于肢体残疾人士的智能轮椅控制,提出了一种基于侧面人脸的头部姿态识别方法。首先利用肤色分割和边缘分割相结合的方法找出人脸区域,然后计算人脸连通区域的质心坐标和面积,以标准正向姿态时的人脸区域质心坐标和面积为参考值,根据活动人脸连通区域质心坐标与参考点的相对位置、欧式距离及面积比特征判别头部姿态。实验结果表明,该方法可以很好的识别向左倾斜、向右倾斜、抬头、低头等头部姿态,平均识别率为92.2%。