Automated skin lesion detection and classification using fused deep convolutional neural network on dermoscopic images

Skin cancer becomes a deadly disease that affect people of all ages globally. The availability of various types of benign and malignant melanoma makes the skin lesion diagnostic process difficult. Since the visual inspection of skin cancer is costlier and lengthy process, it is needed to design automatic diagnosis model to classify skin lesions accurately and promptly. Computer-aided diagnosis models can be employed to identify the presence of skin lesions using dermoscopic images. The automatic identification of skin lesions can assist the doctors and enable the detection process at an efficient and faster rate. With this motivation, this article presents an automated skin lesion detection and classification using fused deep convolutional neural network (ASDC-FDCNN) on dermoscopic images. The ASDC-FDCNN technique aims to identify the existence of skin lesions from dermoscopic images. The ASDC-FDCNN model involves the design of two deep learning models namely VGG19 and ResNet152 models. Besides, the fusion based feature extraction process is performed to derive feature vectors. In addition, the DCNN technique was employed as classifier for identifying the presence or absence of skin lesions. The performance validation of the ASDC-FDCNN technique takes place utilizing benchmark skin lesion dataset. A comparative results analysis reported the better performance of the ASDC-FDCNN model over the recent technique with respect to various measures.     

Deep Learning with Optimal Hierarchical Spiking Neural Network for Medical Image Classification

Medical image classification becomes a vital part of the design of computer aided diagnosis (CAD) models. The conventional CAD models are majorly dependent upon the shapes, colors, and/or textures that are problem oriented and exhibited complementary in medical images. The recently developed deep learning (DL) approaches pave an efficient method of constructing dedicated models for classification problems. But the maximum resolution of medical images and small datasets, DL models are facing the issues of increased computation cost. In this aspect, this paper presents a deep convolutional neural network with hierarchical spiking neural network (DCNN-HSNN) for medical image classification. The proposed DCNN-HSNN technique aims to detect and classify the existence of diseases using medical images. In addition, region growing segmentation technique is involved to determine the infected regions in the medical image. Moreover, NADAM optimizer with DCNN based Capsule Network (CapsNet) approach is used for feature extraction and derived a collection of feature vectors. Furthermore, the shark smell optimization algorithm (SSA) based HSNN approach is utilized for classification process. In order to validate the better performance of the DCNN-HSNN technique, a wide range of simulations take place against HIS2828 and ISIC2017 datasets. The experimental results highlighted the effectiveness of the DCNN-HSNN technique over the recent techniques interms of different measures. Please type your abstract here.     

Toward a combined tool to assist dermatologists in melanoma detection from dermoscopic images of pigmented skin lesions

In this paper we propose a machine learning approach to classify melanocytic lesions as malignant or benign, using dermoscopic images. The lesion features used in the classification framework are inspired on border, texture, color and structures used in popular dermoscopy algorithms performed by clinicians by visual inspection. The main weakness of dermoscopy algorithms is the selection of a set of weights and thresholds, that appear not to be robust or independent of population. The use of machine learning techniques allows to overcome this issue. The proposed method is designed and tested on an image database composed of 655 images of melanocytic lesions: 544 benign lesions and 111 malignant melanoma. After an image pre-processing stage that includes hair removal filtering, each image is automatically segmented using well known image segmentation algorithms. Then, each lesion is characterized by a feature vector that contains shape, color and texture information, as well as local and global parameters. The detection of particular dermoscopic patterns associated with melanoma is also addressed, and its inclusion in the classification framework is discussed. The learning and classification stage is performed using AdaBoost with C4.5 decision trees. For the automatically segmented database, classification delivered a specificity of 77% for a sensitivity of 90%. The same classification procedure applied to images manually segmented by an experienced dermatologist yielded a specificity of 85% for a sensitivity of 90%.     

基于深度学习的盲文识别方法

提出一种基于深度学习的盲文点字识别方法,利用深度模型--堆叠去噪自动编码器（Stack Denoising AutoEncoder,SDAE）解决盲文识别中特征的自动提取与降维等问题。在构建深度模型过程中,采用非监督贪婪逐层训练算法（Greedy Layer Wise Unsupervised Learning Algorithm）初始化网络权重,使用反向传播算法优化网络参数。利用SDAE自动学习盲文点字图片特征,使用Softmax分类器进行识别。实验结果表明,本文所提方法较之传统方法,可以有效解决样本特征的自动学习与特征降维等问题,操作更为简易,并能获得满意的识别结果。     

Big Data Analytics with Optimal Deep Learning Model for Medical Image Classification

In recent years, huge volumes of healthcare data are getting generated in various forms. The advancements made in medical imaging are tremendous owing to which biomedical image acquisition has become easier and quicker. Due to such massive generation of big data, the utilization of new methods based on Big Data Analytics (BDA), Machine Learning (ML), and Artificial Intelligence (AI) have become essential. In this aspect, the current research work develops a new Big Data Analytics with Cat Swarm Optimization based deep Learning (BDA-CSODL) technique for medical image classification on Apache Spark environment. The aim of the proposed BDA-CSODL technique is to classify the medical images and diagnose the disease accurately. BDA-CSODL technique involves different stages of operations such as preprocessing, segmentation, feature extraction, and classification. In addition, BDA-CSODL technique also follows multi-level thresholding-based image segmentation approach for the detection of infected regions in medical image. Moreover, a deep convolutional neural network-based Inception v3 method is utilized in this study as feature extractor. Stochastic Gradient Descent (SGD) model is used for parameter tuning process. Furthermore, CSO with Long Short-Term Memory (CSO-LSTM) model is employed as a classification model to determine the appropriate class labels to it. Both SGD and CSO design approaches help in improving the overall image classification performance of the proposed BDA-CSODL technique. A wide range of simulations was conducted on benchmark medical image datasets and the comprehensive comparative results demonstrate the supremacy of the proposed BDA-CSODL technique under different measures.     

糖尿病性视网膜图像的深度学习分类方法

目的 糖尿病性视网膜病变（DR）是目前比较严重的一种致盲眼病,因此,对糖尿病性视网膜病理图像的自动分类具有重要的临床应用价值。基于人工分类视网膜图像的方法存在判别性特征提取困难、分类性能差、耗时费力且很难得到客观统一的医疗诊断等问题,为此,提出一种基于卷积神经网络和分类器的视网膜病理图像自动分类系统。方法 首先,结合现有的视网膜图像的特点,对图像进行去噪、数据扩增、归一化等预处理操作;其次,在AlexNet网络的基础上,在网络的每一个卷积层和全连接层前引入一个批归一化层,得到一个网络层次更复杂的深度卷积神经网络BNnet。BNnet网络用于视网膜图像的特征提取网络,对其训练时采用迁移学习的策略利用ILSVRC2012数据集对BNnet网络进行预训练,再将训练得到的模型迁移到视网膜图像上再学习,提取用于视网膜分类的深度特征;最后,将提取的特征输入一个由全连接层组成的深度分类器将视网膜图像分为正常的视网膜图像、轻微病变的视网膜图像、中度病变的视网膜图像等5类。结果 实验结果表明,本文方法的分类准确率可达0.93,优于传统的直接训练方法,且具有较好的鲁棒性和泛化性。结论 本文提出的视网膜病理图像分类框架有效地避免了人工特征提取和图像分类的局限性,同时也解决了样本数据不足而导致的过拟合问题。     

基于迁移学习的伤口图像分割

图像分割是计算机辅助阅片的基础, 伤口图像分割的准确率直接影响伤口分析的结果. 传统方法进行伤口分割步骤繁琐, 准确率低. 目前已有少部分人利用深度学习进行伤口图像分割, 但是他们都是基于小型数据集, 难以发挥深度神经网络的优势, 准确率难以进一步提高. 充分发挥深度学习在图像分割领域的优势需要大型数据集, 目前还没有关于伤口图像的大型公共数据集, 而制作大型伤口图像数据集需要人工标记, 耗费大量时间和精力. 本文提出基于迁移学习的伤口图像分割方法, 首先利用大型公共数据集训练ResNet50网络作为特征提取器, 再利用该特征提取器连接上两个并行的注意力机制后在利用小型伤口图像数据集进行再训练. 实验表明本方法的分割结果在平均交并比上有较大提高, 在某种程度上解决了缺乏大型伤口图像数据集而导致伤口图像分割准确率低的问题.     

改进的基于深度学习的遥感图像分类算法

针对传统的基于深度学习的遥感图像分类算法未能有效融合多种深度学习特征，且分类器性能欠佳的问题，提出一种改进的基于深度学习的高分辨率遥感图像分类算法。首先，设计并搭建一个七层卷积神经网络；其次，将高分辨率遥感图像样本输入到该网络中进行网络训练，得到最后两个全连接层输出作为遥感图像两种不同的高层特征；再次，针对该网络第五层池化层输出，采用主成分分析（PCA）进行降维，作为遥感图像的第三种高层特征；然后，将上述三种高层特征通过串联的形式进行融合，得到一种有效的基于深度学习的遥感图像特征；最后，设计了一种基于逻辑回归的遥感图像分类器，可以对遥感图像进行有效分类。与传统基于深度学习的遥感图像分类算法相比，所提算法分类准确率有较高提升。实验结果表明，该算法在分类准确率、误分类率和Kappa系数上表现优异，能实现良好的分类效果。     

融合注意力机制和高效网络的糖尿病视网膜病变识别与分类

目的 糖尿病视网膜病变（diabetic retinopathy,DR）是一种病发率和致盲率都很高的糖尿病并发症。临床中,由于视网膜图像不同等级之间差异性小以及临床医生经验的不同,会出现误诊、漏诊等情况,目前基于人工DR的诊断分类性能差且耗时费力。基于此,本文提出一种融合注意力机制（attention mechanism）和高效率网络（high-efficiency network,EfficientNet）的DR影像自动分类识别方法,以此达到对病变类型的精确诊断。方法 针对实验中DR数据集存在的问题,进行剔除、去噪、扩增和归一化等处理;利用EfficientNet进行特征提取,采用迁移学习的策略用DR的数据集对EfficientNet进行学习与训练,提取深度特征。为了解决病变之间差异小的问题,防止网络对糖尿病视网膜图像的特征学习时出现错分等情况,在EfficientNet输出结果上加入注意力机制;根据网络提取的特征在深度分类器中进行分类,将视网膜图像按等级进行五分类。结果 本文方法的分类精度、敏感性、特异性和二次加权（kappa）值分别为97.2%、95.6%、98.7%和0.84,具有较好的分类性能及鲁棒性。结论 基于融合注意力机制的高效率网络（attention EfficientNet,A-EfficientNet）的DR分类算法有效地提高了DR筛查效率,解决了人工分类的手动提取特征的局限性,在临床上对医生诊断起到了辅助作用,能更有效地防治此类恶性眼疾造成严重视力损伤、甚至失明。     

3D卷积自编码器高光谱图像分类模型

目的 高光谱图像分类是遥感领域的基础问题，高光谱图像同时包含丰富的光谱信息和空间信息，传统模型难以充分利用两种信息之间的关联性，而以卷积神经网络为主的有监督深度学习模型需要大量标注数据，但标注数据难度大且成本高。针对现有模型的不足，本文提出了一种无监督范式下的高光谱图像空谱融合方法，建立了3D卷积自编码器（3D convolutional auto-encoder，3D-CAE）高光谱图像分类模型。方法 3D卷积自编码器由编码器、解码器和分类器构成。将高光谱数据预处理后，输入到编码器中进行无监督特征提取，得到一组特征图。编码器的网络结构为3个卷积块构成的3D卷积神经网络，卷积块中加入批归一化技术防止过拟合。解码器为逆向的编码器，将提取到的特征图重构为原始数据，用均方误差函数作为损失函数判断重构误差并使用Adam算法进行参数优化。分类器由3层全连接层组成，用于判别编码器提取到的特征。以3D-CNN （three dimensional convolutional neural network）为自编码器的主干网络可以充分利用高光谱图像的空间信息和光谱信息，做到空谱融合。以端到端的方式对模型进行训练可以省去复杂的特征工程和数据预处理，模型的鲁棒性和稳定性更强。结果 在Indian Pines、Salinas、Pavia University和Botswana等4个数据集上与7种传统单特征方法及深度学习方法进行了比较，本文方法均取得最优结果，总体分类精度分别为0.948 7、0.986 6、0.986 2和0.964 9。对比实验结果表明了空谱融合和无监督学习对于高光谱遥感图像分类的有效性。结论 本文模型充分利用了高光谱图像的光谱特征和空间特征，可以做到无监督特征提取，无需大量标注数据的同时分类精度高，是一种有效的高光谱图像分类方法。     

基于深度玻尔兹曼机的乐器分类问题研究

应用传统浅层模型处理乐器分类任务存在非线性拟合能力较差的问题,使分类准确率得不到有效保证,有必要引入深度学习方法提升复杂任务的非线性建模能力。将深度玻尔兹曼机作为特征提取器提取表达能力更强的数据特征,分别以SVM与Softmax分类器作为深度神经网络的顶层设置形成DBM SVM组合模型与DBM Softmax组合模型,引入平均场理论和动量项因子优化网络训练过程。将上述两组模型及单一SVM分类器在5类乐器音频数据上进行对比实验,两种深度学习组合模型的分类准确率分别达到89.29%和87.5%,与传统浅层分类方法SVM的73.21%的准确率相比优势明显。实验结果表明深度玻尔兹曼机在乐器分类领域的应用颇具前景。     

Combining neural networks and semantic feature space for email classification

Email is one of the most ubiquitous and pervasive applications used on a daily basis by millions of people worldwide, individuals and organizations more and more rely on the emails to communicate and share information and knowledge. However, the increase in email users has resulted in a dramatic increase in spam emails during the past few years. It is becoming a big challenge to process and manage the emails efficiently for and individuals and organizations. This paper proposes new email classification models using a linear neural network trained by perceptron learning algorithm and a nonlinear neural network trained by back-propagation learning algorithm. An efficient semantic feature space (SFS) method is introduced in these classification models. The traditional back-propagation neural network (BPNN) has slow learning speed and is prone to trap into a local minimum, so the modified back-propagation neural network (MBPNN) is presented to overcome these limitations. The vector space model based email classification system suffers from a large number of features and ambiguity in the meaning of terms, which will lead to sparse and noisy feature space. So we use the SFS to convert the original sparse and noisy feature space to a semantically richer feature space, which will helps to accelerate the learning speed. The experiments are conducted based on different training set size and extracted feature size. Experimental results show that the models using MBPNN outperform the traditional BPNN, and the use of SFS can greatly reduce the feature dimensionality and improve email classification performance.     

面向乳腺超声计算机辅助诊断的两阶段深度迁移学习

目的 为了提升基于单模态B型超声（B超）的乳腺癌计算机辅助诊断（computer-aided diagnosis,CAD）模型性能,提出一种基于两阶段深度迁移学习（two-stage deep transfer learning,TSDTL）的乳腺超声CAD算法,将超声弹性图像中的有效信息迁移至基于B超的乳腺癌CAD模型之中,进一步提升该CAD模型的性能。方法 在第1阶段的深度迁移学习中,提出将双模态超声图像重建任务作为一种自监督学习任务,训练一个关联多模态深度卷积神经网络模型,实现B超图像和超声弹性图像之间的信息交互迁移;在第2阶段的深度迁移学习中,基于隐式的特权信息学习（learning using privilaged information,LUPI）范式,进行基于双模态超声图像的乳腺肿瘤分类任务,通过标签信息引导下的分类进一步加强两个模态之间的特征融合与信息交互;采用单模态B超数据对所对应通道的分类网络进行微调,实现最终的乳腺癌B超图像分类模型。结果 实验在一个乳腺肿瘤双模超声数据集上进行算法性能验证。实验结果表明,通过迁移超声弹性图像的信息,TSDTL在基于B超的乳腺癌诊断任务中取得的平均分类准确率为87.84±2.08%、平均敏感度为88.89±3.70%、平均特异度为86.71±2.21%、平均约登指数为75.60±4.07%,优于直接基于单模态B超训练的分类模型以及多种典型迁移学习算法。结论 提出的TSDTL算法通过两阶段的深度迁移学习,将超声弹性图像的信息有效迁移至基于B超的乳腺癌CAD模型,提升了模型的诊断性能,具备潜在的应用可行性。     

基于平行图像与深度学习的绝缘子目标检测法

绝缘子是输电线路中最易发生故障的部件之一,需及时从大量图像中检测出绝缘子,为检修提供可靠依据。为解决传统方法和深度学习各自存在的局限性,提出一种基于平行图像与深度学习的绝缘子检测方法。首先,建立人工绝缘子图像数据集,并对待测的真实绝缘子图像进行颜色预选处理;然后,构建卷积神经网络进行特征提取和分类,利用正常训练和迁移学习两种方法,并采用消融实验对不同模型进行性能的分析与对比;最终,实现绝缘子目标的检测及模型指标的评价。实验结果表明,颜色预选和平行图像方法均能有效提升模型效果,使得模型的loss能更快速的收敛,分类准确率也有明显的提高,上述方法能够有效检测出图像中的绝缘子,为后期绝缘子故障的检测和处理提供了研究基础。     

基于深度极限学习机的卫星云图云量计算

卫星云图云量计算是卫星气象应用的基础,现阶段对其的研究未能充分利用卫星云图的特征,导致云检测及云量计算的效果不好。针对该问题,利用多层神经网络进行卫星云图的特征提取,并通过大量实验寻找到最优的深度学习的网络结构。基于度极限学习机对卫星云图的云进行检测和分类,再利用“空间相关法”计算云图中的总云量。实验结果表明,基于传统极限学习机的深度极限学习机能够充分提取云图的特征,在进行云分类时能够较清晰地区分厚云和薄云间的界限。相比于传统阈值法、极限学习机模型以及卷积神经网络,深度极限学习机的云识别率以及云量计算准确率更高,且所提方法比卷积神经网络的效率更高。     

Latent Space Representational Learning of Deep Features for Acute Lymphoblastic Leukemia Diagnosis

Acute Lymphoblastic Leukemia (ALL) is a fatal malignancy that is featured by the abnormal increase of immature lymphocytes in blood or bone marrow. Early prognosis of ALL is indispensable for the effectual remediation of this disease. Initial screening of ALL is conducted through manual examination of stained blood smear microscopic images, a process which is time-consuming and prone to errors. Therefore, many deep learning-based computer-aided diagnosis (CAD) systems have been established to automatically diagnose ALL. This paper proposes a novel hybrid deep learning system for ALL diagnosis in blood smear images. The introduced system integrates the proficiency of autoencoder networks in feature representational learning in latent space with the superior feature extraction capability of standard pretrained convolutional neural networks (CNNs) to identify the existence of ALL in blood smears. An augmented set of deep image features are formed from the features extracted by GoogleNet and Inception-v3 CNNs from a hybrid dataset of microscopic blood smear images. A sparse autoencoder network is designed to create an abstract set of significant latent features from the enlarged image feature set. The latent features are used to perform image classification using Support Vector Machine (SVM) classifier. The obtained results show that the latent features improve the classification performance of the proposed ALL diagnosis system over the original image features. Moreover, the classification performance of the system with various sizes of the latent feature set is evaluated. The retrieved results reveal that the introduced ALL diagnosis system superiorly compete the state of the art.     

基于改进型模糊ARTMAP网络的CBR遥感图像分类系统研究

针对难以及时地获取充足而准确的遥感样本、缺乏积累和管理遥感样本的有效手段是制约遥感图像分类技术发展的瓶颈问题。构建了基于改进型模糊ARTMAP网络的CBR(case-based reasoning范例推理)遥感图像分类系统。系统将改进型模糊ARTMAP网络作为范例的知识提取器和图像分类器,运用CBR求解策略实现遥感样本知识的合理储备、优化组合和重复利用。分别应用本文所建系统、最大似然法、BP网络和改进型模糊ARTMAP网络对向海自然保护区TM遥感图像进行分类操作,实验结果表明,本文建立的系统与其他分类方法相比,能够更好地提高遥感样本数据的利用效率和遥感图像的分类精度,而且一定程度上解决了在样本有限的条件下如何高效利用已有数据进行遥感图像分类的问题。     

面向人机交互的手势识别系统设计

在人机交互领域(Human-Computer Interaction,HCI)中,基于视觉的手势识别因其直观、高效的特点拥有广阔的应用前景。为了改善传统手势识别算法识别率低、鲁棒性差的缺点,基于OpenCV和Keras深度学习框架提出一种简单、快速的手势识别方法作为人机交互的接口。手势图像经过3个处理阶段:预处理、特征提取和分类。对输入图像进行预处理,使用YCbCr肤色模型提取出手部肤色区域,将其转化为灰度图像。使用卷积神经网络对手势图像进行特征提取和分类。实验结果表明:提出的手势识别方法识别率很高,达到99.43%,且具有较好的鲁棒性。与传统的人工选取特征相比,卷积神经网络能够有效地进行特征学习。     

Automatic land cover classification of geo-tagged field photos by deep learning

With more and more crowdsourcing geo-tagged field photos available online, they are becoming a potentially valuable source of information for environmental studies. However, the labelling and recognition of these photos are time-consuming. To utilise such information, a land cover type recognition model for field photos was proposed based on the deep learning technique. This model combines a pre-trained convolutional neural network (CNN) as the image feature extractor and the multinomial logistic regression model as the feature classifier. The pre-trained CNN model Inception-v3 was used in this study. The labelled field photos from the Global Geo-Referenced Field Photo Library (http://eomf.ou.edu/photos) were chosen for model training and validation. The results indicated that our recognition model achieved an acceptable accuracy (48.40% for top-1 prediction and 76.24% for top-3 prediction) of land cover classification. With accurate self-assessment of confidence, the model can be applied to classify numerous online geo-tagged field photos for environmental information extraction.