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 autoencoder (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) algorithm. To showcase the enhanced outcomes of the OSSAE-BPNN model, a comprehensive experimental analysis is performed on the benchmark dataset. The experimental findings demonstrated that the OSSAE-BPNN approach outperformed other current strategies in terms of several assessment metrics.     

Stacked Gated Recurrent Unit Classifier with CT Images for Liver Cancer Classification

Liver cancer is one of the major diseases with increased mortality in recent years, across the globe. Manual detection of liver cancer is a tedious and laborious task due to which Computer Aided Diagnosis (CAD) models have been developed to detect the presence of liver cancer accurately and classify its stages. Besides, liver cancer segmentation outcome, using medical images, is employed in the assessment of tumor volume, further treatment plans, and response monitoring. Hence, there is a need exists to develop automated tools for liver cancer detection in a precise manner. With this motivation, the current study introduces an Intelligent Artificial Intelligence with Equilibrium Optimizer based Liver cancer Classification (IAIEO-LCC) model. The proposed IAIEO-LCC technique initially performs Median Filtering (MF)-based pre-processing and data augmentation process. Besides, Kapur’s entropy-based segmentation technique is used to identify the affected regions in liver. Moreover, VGG-19 based feature extractor and Equilibrium Optimizer (EO)-based hyperparameter tuning processes are also involved to derive the feature vectors. At last, Stacked Gated Recurrent Unit (SGRU) classifier is exploited to detect and classify the liver cancer effectively. In order to demonstrate the superiority of the proposed IAIEO-LCC technique in terms of performance, a wide range of simulations was conducted and the results were inspected under different measures. The comparison study results infer that the proposed IAIEO-LCC technique achieved an improved accuracy of 98.52%.     

Computational methods for pigmented skin lesion classification in images: review and future trends

Skin cancer is considered as one of the most common types of cancer in several countries, and its incidence rate has increased in recent years. Melanoma cases have caused an increasing number of deaths worldwide, since this type of skin cancer is the most aggressive compared to other types. Computational methods have been developed to assist dermatologists in early diagnosis of skin cancer. An overview of the main and current computational methods that have been proposed for pattern analysis and pigmented skin lesion classification is addressed in this review. In addition, a discussion about the application of such methods, as well as future trends, is also provided. Several methods for feature extraction from both macroscopic and dermoscopic images and models for feature selection are introduced and discussed. Furthermore, classification algorithms and evaluation procedures are described, and performance results for lesion classification and pattern analysis are given.

     

A Framework of Deep Learning and Selection-Based Breast Cancer Detection from Histopathology Images

Breast cancer (BC) is a most spreading and deadly cancerous malady which is mostly diagnosed in middle-aged women worldwide and effecting beyond a half-million people every year. The BC positive newly diagnosed cases in 2018 reached 2.1 million around the world with a death rate of 11.6% of total cases. Early diagnosis and detection of breast cancer disease with proper treatment may reduce the number of deaths. The gold standard for BC detection is biopsy analysis which needs an expert for correct diagnosis. Manual diagnosis of BC is a complex and challenging task. This work proposed a deep learning-based (DL) solution for the early detection of this deadly disease from histopathology images. To evaluate the robustness of the proposed method a large publically available breast histopathology image database containing a total of 277524 histopathology images is utilized. The proposed automatic diagnosis of BC detection and classification mainly involves three steps. Initially, a DL model is proposed for feature extraction. Secondly, the extracted feature vector (FV) is passed to the proposed novel feature selection (FS) framework for the best FS. Finally, for the classification of BC into invasive ductal carcinoma (IDC) and normal class different machine learning (ML) algorithms are used. Experimental outcomes of the proposed methodology achieved the highest accuracy of 92.7% which shows that the proposed technique can successfully be implemented for BC detection to aid the pathologists in the early and accurate diagnosis of BC.     

基于Self-Attention StyleGAN的皮肤癌图像生成与分类

针对以黑色素瘤为代表的皮肤癌分类任务存在数据集各类样本数量、权重不均衡,且现有的对抗生成网络生成的皮肤癌样本图像质量较差导致临床诊断时难以分辨等问题,提出了一种基于自注意力的样式生成对抗网络（Self-Attention StyleGAN）与SE-ResNeXt-50相结合的皮肤癌图像样本生成与分类框架。该框架在样式生成对抗网络（StyleGAN）的基础上引入了自注意力机制,对生成器的样式控制和噪声输入结构进行了重新设计,并重构了鉴别器对图像生成器进行了调整,从而有效地合成高质量的皮肤癌病变图像。使用SE-ResNeXt-50来对皮肤癌样本图像进行分类,更好地提取样本图像不同层次特征图的信息,从而提高了平衡多类精度（BMA）。实验结果表明,该模型在ISIC2019皮肤癌数据集上生成的样本图像质量较高,且分类BMA达到94.71%。该方法提高了皮肤癌病变图像分类的准确性,帮助皮肤科医生对不同类型的皮肤癌病变进行判断和诊断,并对不同阶段和难以区分的皮肤癌病变进行分析。     

深度学习多部位病灶检测与分割

目的 多部位病灶具有大小各异和类型多样的特点,对其准确检测和分割具有一定的难度。为此,本文设计了一种2.5D深度卷积神经网络模型,实现对多种病灶类型的计算机断层扫描（computed tomography,CT）图像的病灶检测与分割。方法 利用密集卷积网络和双向特征金字塔网络组成的骨干网络提取图像中的多尺度和多维度信息,输入为带有标注的中央切片和提供空间信息的相邻切片共同组合而成的CT切片组。将融合空间信息的特征图送入区域建议网络并生成候选区域样本,再由多阈值级联网络组成的Cascade R-CNN（region convolutional neural networks）筛选高质量样本送入检测与分割分支进行训练。结果 本文模型在DeepLesion数据集上进行验证。结果表明,在测试集上的平均检测精度为83.15%,分割预测结果与真实标签的端点平均距离误差为1.27 mm,直径平均误差为1.69 mm,分割性能优于MULAN（multitask universal lesion analysis network for joint lesion detection,tagging and segmentation）和Auto RECIST（response evaluation criteria in solid tumors）,且推断每幅图像平均时间花费仅91.7 ms。结论 对于多种部位的CT图像,本文模型取得良好的检测与分割性能,并且预测时间花费较少,适用病变类别与DeepLesion数据集类似的CT图像实现病灶检测与分割。本文模型在一定程度上能满足医疗人员利用计算机分析多部位CT图像的需求。     

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。     

Content-based image retrieval techniques for the analysis of dermatological lesions using particle swarm optimization technique

This method presents extraction of effective color and shape features for the analysis of dermatology images. We employ three phases of operation in order to perform efficient retrieval of images of skin lesions. Our proposed algorithm used color and shape feature vectors and the features are normalized using Min–Max normalization. Particle swarm optimization (PSO) technique for multi-class classification is used to converge the search space more efficiently. The results using receiver operating characteristic (ROC) curve proved that the proposed architecture is highly contributed to computer-aided diagnosis of skin lesions. Experiments on a set of 1450 images yielded a specificity of 98.22% and a sensitivity of 94%. Our empirical evaluation has a superior retrieval and diagnosis performance when compared to the performance of other works. We present explicit combinations of feature vectors corresponding to healthy and lesion skin.     

结合域适应学习的糖尿病视网膜病变分级诊断

目的 传统的糖尿病视网膜病变(糖网)(diabetic retinopathy, DR)依赖于早期病理特征的精确检测,但由于数据集缺乏病灶标记区域导致无法有效地建立监督性分类模型,引入其他辅助数据集又会出现跨域数据异质性问题;另外,现有的糖网诊断方法大多无法直观地从语义上解释医学模型预测的结果。基于此,本文提出一种端到端式结合域适应学习的糖网自动多分类方法,该方法协同注意力机制和弱监督学习加强优化。方法 首先,利用已标记病灶区域的辅助数据训练病灶检测模型,再将目标域数据集的糖网诊断转化为弱监督学习问题,依靠多分类预测结果指导深度跨域生成对抗网络模型,提升跨域的样本图像质量,用于微调病灶检测模型,进而过滤目标域中一些无关的病灶样本,提升多分类分级诊断性能。最后,在整体模型中融合注意力机制,从医学病理诊断角度提供可解释性支持其分类决策。结果 在公开数据集Messidor上进行糖网多分类评估实验,本文方法获得了71.2%的平均准确率和80.8%的AUC(area under curve)值,相比于其他多种方法具有很大优势,可以辅助医生进行临床眼底筛查。结论 结合域适应学习的糖网分类方法在没有...     

Optimised deep learning features for improved melanoma detection

In this article, we are addressing the question of effective usage of the feature set extracted from deep learning models pre-trained on ImageNet. Exploring this option will offer very fast and attractive alternative to transfer learning strategies. The traditional task of skin lesion recognition consists of several stages, where the automated system is typically trained on preprocessed images with known diagnosis, which allows classification of new samples to predefined categories. For this task, we are proposing here an improved melanoma detection method based on the combination of linear discriminant analysis (LDA) and the features extracted from the deep learning approach. We are examining the usage of the LDA approach on activation of the fully-connected layer of deep learning in order to increase the classification accuracy and at the same time to reduce the feature space dimensionality. We tested our method on five different classifiers and evaluated results using various metrics. The presented comparison demonstrates the very high effectiveness of the suggested feature reduction, which leads not only to the significant lowering of employed features but also to the increasing performance of all tested classifiers in almost all measured characteristics.

     

Bagged textural and color features for melanoma skin cancer detection in dermoscopic and standard images

Early detection of malignant melanoma skin cancer is crucial for treating the disease and saving lives. Many computerized techniques have been reported in the literature to diagnose and classify the disease with satisfactory skin cancer detection performance. However, reducing the false detection rate is still challenging and preoccupying because false positives trigger the alarm and require intervention by an expert pathologist for further examination and screening. In this paper, an automatic skin cancer diagnosis system that combines different textural and color features is proposed. New textural and color features are used in a bag-of-features approach for efficient and accurate detection. We particularly claim that the Histogram of Gradients (HG) and the Histogram of Lines (HL) are more suitable for the analysis and classification of dermoscopic and standard skin images than the conventional Histogram of Oriented Gradient (HOG) and the Histogram of Oriented Lines (HOL), respectively. The HG and HL are bagged separately using a codebook for each and then combined with other bagged color vector angles and Zernike moments to exploit the color information. The overall system has been assessed through intensive experiments using different classifiers on a dermoscopic image dataset and another standard dataset. Experimental results have shown the superiority of the proposed system over state-of-the-art techniques.     

Intelligent Beetle Antenna Search with Deep Transfer Learning Enabled Medical Image Classification Model

Recently, computer assisted diagnosis (CAD) model creation has become more dependent on medical picture categorization. It is often used to identify several conditions, including brain disorders, diabetic retinopathy, and skin cancer. Most traditional CAD methods relied on textures, colours, and forms. Because many models are issue-oriented, they need a more substantial capacity to generalize and cannot capture high-level problem domain notions. Recent deep learning (DL) models have been published, providing a practical way to develop models specifically for classifying input medical pictures. This paper offers an intelligent beetle antenna search (IBAS-DTL) method for classifying medical images facilitated by deep transfer learning. The IBAS-DTL model aims to recognize and classify medical pictures into various groups. In order to segment medical pictures, the current IBASDTLM model first develops an entropy based weighting and first-order cumulative moment (EWFCM) approach. Additionally, the DenseNet-121 technique was used as a module for extracting features. A BAS with an extreme learning machine (ELM) model is used to classify the medical photos. A wide variety of tests were carried out using a benchmark medical imaging dataset to demonstrate the IBAS-DTL model’s noteworthy performance. The results gained indicated the IBAS-DTL model’s superiority over its pre-existing techniques.     

Leveraging Multimodal Ensemble Fusion-Based Deep Learning for COVID-19 on Chest Radiographs

Recently, COVID-19 has posed a challenging threat to researchers, scientists, healthcare professionals, and administrations over the globe, from its diagnosis to its treatment. The researchers are making persistent efforts to derive probable solutions for managing the pandemic in their areas. One of the widespread and effective ways to detect COVID-19 is to utilize radiological images comprising X-rays and computed tomography (CT) scans. At the same time, the recent advances in machine learning (ML) and deep learning (DL) models show promising results in medical imaging. Particularly, the convolutional neural network (CNN) model can be applied to identifying abnormalities on chest radiographs. While the epidemic of COVID-19, much research is led on processing the data compared with DL techniques, particularly CNN. This study develops an improved fruit fly optimization with a deep learning-enabled fusion (IFFO-DLEF) model for COVID-19 detection and classification. The major intention of the IFFO-DLEF model is to investigate the presence or absence of COVID-19. To do so, the presented IFFO-DLEF model applies image pre-processing at the initial stage. In addition, the ensemble of three DL models such as DenseNet169, EfficientNet, and ResNet50, are used for feature extraction. Moreover, the IFFO algorithm with a multilayer perceptron (MLP) classification model is utilized to identify and classify COVID-19. The parameter optimization of the MLP approach utilizing the IFFO technique helps in accomplishing enhanced classification performance. The experimental result analysis of the IFFO-DLEF model carried out on the CXR image database portrayed the better performance of the presented IFFO-DLEF model over recent approaches.     

A retrieval system to analyse dermatological lesions using feature ortho-normalisation

An information retrieval system is proposed as an assistance tool for diagnosing the skin lesion using Content-Based Image Retrieval approach. Efficiency of the retrieval system is deliberated in terms of the most relevant retrieval of images from database. The proposed diagnostic assistive model retrieves the skin lesion images and its disease category, case history, symptoms and treatment plan. This retrieval process is made from a dermatology database by the way of visual features in the input image such as shape, texture and colour. The author’s proposed principal component analysis (PCA) feature projection technique is to discriminate the features by projecting them onto a feature subspace. While projecting the features onto a feature subspace features are normalised orthogonally. So the proposed methodology is used to improve the classification by the way of discriminate the features, in-turn it focus the retrieval of comprehensive reference sources, so that the diagnosis accuracy of the dermatologists are also improved. Receiver-operating characteristic curve is used to analyse the proposed computer-aided diagnosis (CAD) method, while analysis we attained high contribution to detect the skin lesions. Totally 1450 images are experimented and the system produced the 99.09% specificity, 96.69% sensitivity and 98.3% accuracy. When compared with other works this system of assessment shows high retrieval and diagnosis concert.     

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。     

A computational approach for detecting pigmented skin lesions in macroscopic images

Skin cancer is considered one of the most common types of cancer in several countries and its incidence rate has increased in recent years. Computational methods have been developed to assist dermatologists in early diagnosis of skin cancer. Computational analysis of skin lesion images has become a challenging research area due to the difficulty in discerning some types of skin lesions. A novel computational approach is presented for extracting skin lesion features from images based on asymmetry, border, colour and texture analysis, in order to diagnose skin lesion types. The approach is based on an anisotropic diffusion filter, an active contour model without edges and a support vector machine. Experiments were performed regarding the segmentation and classification of pigmented skin lesions in macroscopic images, with the results obtained being very promising.     

特征重用和注意力机制下肝肿瘤自动分类

目的 肝肿瘤分类计算机辅助诊断技术在临床医学中具有重要意义,但样本缺乏、标注成本高及肝脏图像的敏感性等原因,限制了深度学习的分类潜能,使得肝肿瘤分类依然是医学图像处理领域中具有挑战性的任务。针对上述问题,本文提出了一种结合特征重用和注意力机制的肝肿瘤自动分类方法。方法 利用特征重用模块对计算机断层扫描（computed tomography,CT）图像进行伪自然图像的预处理,复制经Hounsfield处理后的原通道信息,并通过数据增强扩充现有数据;引入基于注意力机制的特征提取模块,从全局和局部两个方面分别对原始数据进行加权处理,充分挖掘现有样本的高维语义特征;通过迁移学习的训练策略训练提出的网络模型,并使用Softmax分类器实现肝肿瘤的精准分类。结果 在120个病人的514幅CT扫描切片上进行了综合实验。与基准方法相比,本文方法平均分类准确率为87.78%,提高了9.73%;与肝肿瘤分类算法相比,本文算法针对转移性肝腺癌、血管瘤、肝细胞癌及正常肝组织的分类召回率分别达到79.47%、79.67%、85.73%和98.31%;与主流分类模型相比,本文模型在多种评价指标中均表现优异,平均准确率、召回率、精确率、F1-score及AUC（area under ROC curve）分别为87.78%、84.43%、84.59%、84.44%和97.50%。消融实验表明了本文设计的有效性。结论 本文方法能提高肝脏肿瘤的分类结果,可为临床诊断提供依据。     

EVALUATING FLUORESCENCE ILLUMINATION TECHNIQUES FOR SKIN LESION DIAGNOSIS

New illumination and imaging techniques are continually being developed for cancer diagnosis. They need to be evaluated in the framework of a specific diagnostic problem. In this work, we evaluate the usefulness of fluorescence illumination within the framework of skin cancer diagnosis. This illumination provides monochrome images that encode certain information about deep layers of the skin, which can be particularly interesting for the diagnosis of skin lesions such as basal cell carcinoma. A broad study of candidate diagnostic features extracted from fluorescence images and evaluated within the framework of the posed diagnostic problem was conducted. Afterward, we used both a genetic algorithm (GA) and forward and backward scanning methods for feature selection and evaluated the diagnostic results by using the K-nearest neighbors (KNN) classifier. This work validates the fluorescence illumination technique for skin cancer diagnosis, indicating concrete image processing techniques that best target the diagnostic problem, and shows that the GA approach obtains the best classification results.     

Intrusion Detection System for Big Data Analytics in IoT Environment

In the digital area, Internet of Things (IoT) and connected objects generate a huge quantity of data traffic which feeds big data analytic models to discover hidden patterns and detect abnormal traffic. Though IoT networks are popular and widely employed in real world applications, security in IoT networks remains a challenging problem. Conventional intrusion detection systems (IDS) cannot be employed in IoT networks owing to the limitations in resources and complexity. Therefore, this paper concentrates on the design of intelligent metaheuristic optimization based feature selection with deep learning (IMFSDL) based classification model, called IMFSDL-IDS for IoT networks. The proposed IMFSDL-IDS model involves data collection as the primary process utilizing the IoT devices and is preprocessed in two stages: data transformation and data normalization. To manage big data, Hadoop ecosystem is employed. Besides, the IMFSDL-IDS model includes a hill climbing with moth flame optimization (HCMFO) for feature subset selection to reduce the complexity and increase the overall detection efficiency. Moreover, the beetle antenna search (BAS) with variational autoencoder (VAE), called BAS-VAE technique is applied for the detection of intrusions in the feature reduced data. The BAS algorithm is integrated into the VAE to properly tune the parameters involved in it and thereby raises the classification performance. To validate the intrusion detection performance of the IMFSDL-IDS system, a set of experimentations were carried out on the standard IDS dataset and the results are investigated under distinct aspects. The resultant experimental values pointed out the betterment of the IMFSDL-IDS model over the compared models with the maximum accuracy 95.25% and 97.39% on the applied NSL-KDD and UNSW-NB15 dataset correspondingly.     

Performance Analysis of Breast Cancer Detection Method Using ANFIS Classification Approach

Breast cancer is one of the deadly diseases prevailing in women. Earlier detection and diagnosis might prevent the death rate. Effective diagnosis of breast cancer remains a significant challenge, and early diagnosis is essential to avoid the most severe manifestations of the disease. The existing systems have computational complexity and classification accuracy problems over various breast cancer databases. In order to overcome the above-mentioned issues, this work introduces an efficient classification and segmentation process. Hence, there is a requirement for developing a fully automatic methodology for screening the cancer regions. This paper develops a fully automated method for breast cancer detection and segmentation utilizing Adaptive Neuro Fuzzy Inference System (ANFIS) classification technique. This proposed technique comprises preprocessing, feature extraction, classifications, and segmentation stages. Here, the wavelet-based enhancement method has been employed as the preprocessing method. The texture and statistical features have been extracted from the enhanced image. Then, the ANFIS classification algorithm is used to classify the mammogram image into normal, benign, and malignant cases. Then, morphological processing is performed on malignant mammogram images to segment cancer regions. Performance analysis and comparisons are made with conventional methods. The experimental result proves that the proposed ANFIS algorithm provides better classification performance in terms of higher accuracy than the existing algorithms.