Automatic COVID-19 detection from X-ray images using ensemble learning with convolutional neural network

Pattern Analysis and Applications - COVID-19 continues to have catastrophic effects on the lives of human beings throughout the world. To combat this disease it is necessary to screen the affected...     

Detection and classification of lung nodules in chest X-ray images using deep convolutional neural networks

Lung nodule classification is one of the main topics related to computer-aided detection systems. Although convolutional neural networks (CNNs) have been demonstrated to perform well on many tasks, there are few explorations of their use for classifying lung nodules in chest X-ray (CXR) images. In this work, we proposed and analyzed a pipeline for detecting lung nodules in CXR images that includes lung area segmentation, potential nodule localization, and nodule candidate classification. We presented a method for classifying nodule candidates with a CNN trained from the scratch. The effectiveness of our method relies on the selection of data augmentation parameters, the design of a specialized CNN architecture, the use of dropout regularization on the network, inclusive in convolutional layers, and addressing the lack of nodule samples compared to background samples balancing mini-batches on each stochastic gradient descent iteration. All model selection decisions were taken using a CXR subset of the Lung Image Database Consortium and Image Database Resource Initiative dataset separately. Thus, we used all images with nodules in the Japanese Society of Radiological Technology dataset for evaluation. Our experiments showed that CNNs were capable of achieving competitive results when compared to state-of-the-art methods. Our proposal obtained an area under the free-response receiver operating characteristic curve of 7.76 considering 10 false positives per image (FPPI), and sensitivity values of 73.1% and 79.6% with 2 and 5 FPPI, respectively.     

Detection of novel coronavirus from chest X-rays using deep convolutional neural networks

Multimedia Tools and Applications - With over 172 Million people infected with the novel coronavirus (COVID-19) globally and with the numbers increasing exponentially, the dire need of a fast...     

Fast and robust multiple ColorChecker detection using deep convolutional neural networks

ColorCheckers are reference standards that professional photographers and filmmakers use to ensure predictable results under every lighting condition. The objective of this work is to propose a new fast and robust method for automatic ColorChecker detection. The process is divided into two steps: (1) ColorCheckers localization and (2) ColorChecker patches recognition. For the ColorChecker localization, we trained a detection convolutional neural network using synthetic images. The synthetic images are created with the 3D models of the ColorChecker and different background images. The output of the neural networks are the bounding box of each possible ColorChecker candidates in the input image. Each bounding box defines a cropped image which is evaluated by a recognition system, and each image is canonized with regards to color and dimensions. Subsequently, all possible color patches are extracted and grouped with respect to the center's distance. Each group is evaluated as a candidate for a ColorChecker part, and its position in the scene is estimated. Finally, a cost function is applied to evaluate the accuracy of the estimation. The method is tested using real and synthetic images. The proposed method is fast, robust to overlaps and invariant to affine projections. The algorithm also performs well in case of multiple ColorCheckers detection.     

Stacked ensemble learning based on deep convolutional neural networks for pediatric pneumonia diagnosis using chest X-ray images

Neural Computing and Applications - Pneumonia is an acute respiratory infection caused by bacteria, viruses, or fungi and has become very common in children ranging from 1 to 5 years of...     

深度全卷积网络的IVUS图像内膜与中—外膜边界检测

目的 心血管内超声（IVUS）图像内膜和中—外膜（MA）轮廓勾画是冠脉粥样硬化和易损斑块定量评估的必要过程。由于存在斑点噪声、图像伪影和各类斑块,重要组织边界的自动分割是一个非常困难的任务。为此,提出一种用于检测20 MHz心电门控IVUS图像内膜和MA边界方法。方法 首先利用深度全卷积网络（DFCN）学习原始IVUS图像与所对应手动分割图像之间映射,预测出目标或者背景的概率图,实现医学图像语义分割。然后在此基础上,结合心血管先验形状信息,采用数学形态学闭、开操作,平滑内膜和MA边界,降低分割过程中错误分类像素或区域的影响。结果 针对来自10位病人的IVUS图像及其标注信息所组成的435幅国际标准公开数据集,从线性回归、Bland-Altman分析和面积交并比（JM）、面积差异百分比（PAD）、Hausdorff距离（HD）、平均距离（AD）等性能指标上,评价本文方法。实验结果表明,算法检测结果与手动勾画结果的相关性可达到0.94,其超过94.71%的结果落在95%置信区域内,具有良好一致性。内膜和MA边界的AD指标分别为：0.07 mm和0.08 mm;HD指标分别为：0.21 mm和0.30 mm。JM指标分别为0.92和0.93;PAD指标分别为5%和4%。此外,对临床所采集的100幅IVUS图像进行了测试,证明本文学习的模型在跨数据集上具有较好的泛化能力。结论 与现有的国际算法比较,本文方法提高了各类斑块、声影区域和血管分支等因素的识别能力,不受超声斑点的影响,能准确地、可重复地检测出IVUS图像中的关键目标边界。     

Machine learning-based automatic detection of novel coronavirus (COVID-19) disease

Multimedia Tools and Applications - Abstract The pandemic was announced by the world health organization coronavirus (COVID-19) universal health dilemma. Any scientific appliance which contributes...     

Differentiation of COVID-19 conditions in planar chest radiographs using optimized convolutional neural networks

In this study, an attempt has been made to differentiate Novel Coronavirus-2019 (COVID-19) conditions from healthy subjects in Chest radiographs using a simplified end-to-end Convolutional Neural Network (CNN) model and occlusion sensitivity maps. Early detection and faster automated screening of the COVID-19 patients is essential. For this, the images are considered from publicly available datasets. Significant biomarkers representing critical image features are extracted from CNN by experimentally investigating on cross-validation methods and hyperparameter settings. The performance of the network is evaluated using standard metrics. Perturbation based occlusion sensitivity maps are employed on the features obtained from the classification model to visualise the localization of abnormal areas. Results demonstrate that the simplified CNN model with optimised parameters is able to extract significant features with a sensitivity of 97.35% and F-measure of 96.71% to detect COVID-19 images. The algorithm achieves an Area Under the Curve-Receiver Operating Characteristic score of 99.4% with Matthews correlation coefficient of 0.93. High value of Diagnostic odds ratio is also obtained. Occlusion sensitivity maps provide precise localization of abnormal regions by identifying COVID-19 conditions. As early detection through chest radiographic images are useful for automated screening of the disease, this method appears to be clinically relevant in providing a visual diagnostic solution using a simplified and efficient model.

     

遥感图像飞机目标分类的卷积神经网络方法

目的 遥感图像飞机目标分类,利用可见光遥感图像对飞机类型进行有效区分,对提供军事作战信息有重要意义。针对该问题,目前存在一些传统机器学习方法,但这些方法需人工提取特征,且难以适应真实遥感图像的复杂背景。近年来,深度卷积神经网络方法兴起,网络能自动学习图像特征且泛化能力强,在计算机视觉各领域应用广泛。但深度卷积神经网络在遥感图像飞机分类问题上应用少见。本文旨在将深度卷积神经网络应用于遥感图像飞机目标分类问题。方法 在缺乏公开数据集的情况下,收集了真实可见光遥感图像中的8种飞机数据,按大致4∶1的比例分为训练集和测试集,并对训练集进行合理扩充。然后针对遥感图像与飞机分类的特殊性,结合深度学习卷积神经网络相关理论,有的放矢地设计了一个5层卷积神经网络。结果 首先,在逐步扩充的训练集上分别训练该卷积神经网络,并分别用同一测试集进行测试,实验表明训练集扩充有利于网络训练,测试准确率从72.4%提升至97.2%。在扩充后训练集上,分别对经典传统机器学习方法、经典卷积神经网络LeNet-5和本文设计的卷积神经网络进行训练,并在同一测试集上测试,实验表明该卷积神经网络的分类准确率高于其他两种方法,最终能在测试集上达到97.2%的准确率,其余两者准确率分别为82.3%、88.7%。结论 在少见使用深度卷积神经网络的遥感图像飞机目标分类问题上,本文设计了一个5层卷积神经网络加以应用。实验结果表明,该网络能适应图像场景,自动学习特征,分类效果良好。     

Corona-Nidaan: lightweight deep convolutional neural network for chest X-Ray based COVID-19 infection detection

The coronavirus COVID-19 pandemic is today’s major public health crisis, we have faced since the Second World War. The pandemic is spreading around the globe like a wave, and according to the World Health Organization’s recent report, the number of confirmed cases and deaths are rising rapidly. COVID-19 pandemic has created severe social, economic, and political crises, which in turn will leave long-lasting scars. One of the countermeasures against controlling coronavirus outbreak is specific, accurate, reliable, and rapid detection technique to identify infected patients. The availability and affordability of RT-PCR kits remains a major bottleneck in many countries, while handling COVID-19 outbreak effectively. Recent findings indicate that chest radiography anomalies can characterize patients with COVID-19 infection. In this study, Corona-Nidaan, a lightweight deep convolutional neural network (DCNN), is proposed to detect COVID-19, Pneumonia, and Normal cases from chest X-ray image analysis; without any human intervention. We introduce a simple minority class oversampling method for dealing with imbalanced dataset problem. The impact of transfer learning with pre-trained CNNs on chest X-ray based COVID-19 infection detection is also investigated. Experimental analysis shows that Corona-Nidaan model outperforms prior works and other pre-trained CNN based models. The model achieved 95% accuracy for three-class classification with 94% precision and recall for COVID-19 cases. While studying the performance of various pre-trained models, it is also found that VGG19 outperforms other pre-trained CNN models by achieving 93% accuracy with 87% recall and 93% precision for COVID-19 infection detection. The model is evaluated by screening the COVID-19 infected Indian Patient chest X-ray dataset with good accuracy.

     

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.     

SegGraph: 室外场景三维点云闭环检测算法

场景流是连续动态场景之间的3D运动场,广泛应用于机器人技术和自动驾驶任务.现有方法忽略了点云点的相关性,仅关注源点云和目标点云逐点的匹配关系,由于匹配关系完全依赖于点云数据的特征信息,导致在局部特征信息不足的点上准确估计场景流仍然存在挑战.根据源点云相邻点具有相关性的特性,提出NCPUM（neighborhood consistency propagation update method）方法,在邻域内将场景流从高置信度点向低置信度点传播,从而优化局部特征信息不足点的场景流.具体来说,NCPUM包含2个模块：置信度预测模块,根据场景流先验分布图,预测源点云逐点的置信度;场景流传播模块,根据局部区域一致性的约束更新低置信度点集的场景流.NCPUM在合成数据集Flyingthings3D和真实驾驶场景数据集KITTI上进行评估,准确度上达到了国际先进水平.由于邻域一致性更符合真实激光雷达场景的先验假设,因此在KITTI数据集上的提升更加明显.

     

Detection of COVID-19 from chest X-ray images: Boosting the performance with convolutional neural network and transfer learning

Coronavirus disease (COVID-19) is a pandemic that has caused thousands of casualties and impacts all over the world. Most countries are facing a shortage of COVID-19 test kits in hospitals due to the daily increase in the number of cases. Early detection of COVID-19 can protect people from severe infection. Unfortunately, COVID-19 can be misdiagnosed as pneumonia or other illness and can lead to patient death. Therefore, in order to avoid the spread of COVID-19 among the population, it is necessary to implement an automated early diagnostic system as a rapid alternative diagnostic system. Several researchers have done very well in detecting COVID-19; however, most of them have lower accuracy and overfitting issues that make early screening of COVID-19 difficult. Transfer learning is the most successful technique to solve this problem with higher accuracy. In this paper, we studied the feasibility of applying transfer learning and added our own classifier to automatically classify COVID-19 because transfer learning is very suitable for medical imaging due to the limited availability of data. In this work, we proposed a CNN model based on deep transfer learning technique using six different pre-trained architectures, including VGG16, DenseNet201, MobileNetV2, ResNet50, Xception, and EfficientNetB0. A total of 3886 chest X-rays (1200 cases of COVID-19, 1341 healthy and 1345 cases of viral pneumonia) were used to study the effectiveness of the proposed CNN model. A comparative analysis of the proposed CNN models using three classes of chest X-ray datasets was carried out in order to find the most suitable model. Experimental results show that the proposed CNN model based on VGG16 was able to accurately diagnose COVID-19 patients with 97.84% accuracy, 97.90% precision, 97.89% sensitivity, and 97.89% of F1-score. Evaluation of the test data shows that the proposed model produces the highest accuracy among CNNs and seems to be the most suitable choice for COVID-19 classification. We believe that in this pandemic situation, this model will support healthcare professionals in improving patient screening.     

Smile detection in the wild with deep convolutional neural networks

Smile or happiness is one of the most universal facial expressions in our daily life. Smile detection in the wild is an important and challenging problem, which has attracted a growing attention from affective computing community. In this paper, we present an efficient approach for smile detection in the wild with deep learning. Different from some previous work which extracted hand-crafted features from face images and trained a classifier to perform smile recognition in a two-step approach, deep learning can effectively combine feature learning and classification into a single model. In this study, we apply the deep convolutional network, a popular deep learning model, to handle this problem. We construct a deep convolutional network called Smile-CNN to perform feature learning and smile detection simultaneously. Experimental results demonstrate that although a deep learning model is generally developed for tackling “big data,” the model can also effectively deal with “small data.” We further investigate into the discriminative power of the learned features, which are taken from the neuron activations of the last hidden layer of our Smile-CNN. By using the learned features to train an SVM or AdaBoost classifier, we show that the learned features have impressive discriminative ability. Experiments conducted on the GENKI4K database demonstrate that our approach can achieve a promising performance in smile detection.     

A new deep learning pipeline to detect Covid-19 on chest X-ray images using local binary pattern,dual tree complex wavelet transform and convolutional neural networks

In this study, which aims at early diagnosis of Covid-19 disease using X-ray images, the deep-learning approach, a state-of-the-art artificial intelligence method, was used, and automatic classification of images was performed using convolutional neural networks (CNN). In the first training-test data set used in the study, there were 230 X-ray images, of which 150 were Covid-19 and 80 were non-Covid-19, while in the second training-test data set there were 476 X-ray images, of which 150 were Covid-19 and 326 were non-Covid-19. Thus, classification results have been provided for two data sets, containing predominantly Covid-19 images and predominantly non-Covid-19 images, respectively. In the study, a 23-layer CNN architecture and a 54-layer CNN architecture were developed. Within the scope of the study, the results were obtained using chest X-ray images directly in the training-test procedures and the sub-band images obtained by applying dual tree complex wavelet transform (DT-CWT) to the above-mentioned images. The same experiments were repeated using images obtained by applying local binary pattern (LBP) to the chest X-ray images. Within the scope of the study, four new result generation pipeline algorithms having been put forward additionally, it was ensured that the experimental results were combined and the success of the study was improved. In the experiments carried out in this study, the training sessions were carried out using the k-fold cross validation method. Here the k value was chosen as 23 for the first and second training-test data sets. Considering the average highest results of the experiments performed within the scope of the study, the values of sensitivity, specificity, accuracy, F-1 score, and area under the receiver operating characteristic curve (AUC) for the first training-test data set were 0,9947, 0,9800, 0,9843, 0,9881 and 0,9990 respectively; while for the second training-test data set, they were 0,9920, 0,9939, 0,9891, 0,9828 and 0,9991; respectively. Within the scope of the study, finally, all the images were combined and the training and testing processes were repeated for a total of 556 X-ray images comprising 150 Covid-19 images and 406 non-Covid-19 images, by applying 2-fold cross. In this context, the average highest values of sensitivity, specificity, accuracy, F-1 score, and AUC for this last training-test data set were found to be 0,9760, 1,0000, 0,9906, 0,9823 and 0,9997; respectively.

     

COVID-19 diagnosis on CT images with Bayes optimization-based deep neural networks and machine learning algorithms

Neural Computing and Applications - Early diagnosis of COVID-19, the new coronavirus disease, is considered important for the treatment and control of this disease. The diagnosis of COVID-19 is...     

3D多尺度深度卷积神经网络肺结节检测

目的 肺结节是肺癌的早期存在形式。低剂量CT（computed tomogragphy）扫描作为肺癌筛查的重要检查手段,已经大规模应用于健康体检,但巨大的CT数据带来了大量工作,随着人工智能技术的快速发展,基于深度学习的计算机辅助肺结节检测引起了关注。由于肺结节尺寸差别较大,在多个尺度上表示特征对结节检测任务至关重要。针对结节尺寸差别较大导致的结节检测困难问题,提出一种基于深度卷积神经网络的胸部CT序列图像3D多尺度肺结节检测方法。方法 包括两阶段：1）尽可能提高敏感度的结节初检网络;2）尽可能减少假阳性结节数量的假阳性降低网络。在结节初检网络中,以组合了压缩激励单元的Res2Net网络为骨干结构,使同一层卷积具有多种感受野,提取肺结节的多尺度特征信息,并使用引入了上下文增强模块和空间注意力模块的区域推荐网络结构,确定候选区域;在由Res2Net网络模块和压缩激励单元组成的假阳性降低网络中对候选结节进一步分类,以降低假阳性,获得最终结果。结果 在公共数据集LUNA16（lung nodule analysis 16）上进行实验,实验结果表明,对于结节初检网络阶段,当平均每例假阳性个数为22时,敏感度可达到0.983,相比基准ResNet + FPN（feature pyramid network）方法,平均敏感度和最高敏感度分别提高了2.6%和0.8%;对于整个3D多尺度肺结节检测网络,当平均每例假阳性个数为1时,敏感度为0.924。结论 与现有主流方案相比,该检测方法不但提高了肺结节检测的敏感度,还有效地控制了假阳性,取得了更优的性能。     

A multi-class skin Cancer classification using deep convolutional neural networks

Skin Cancer accounts for one-third of all diagnosed cancers worldwide. The prevalence of skin cancers have been rising over the past decades. In recent years, use of dermoscopy has enhanced the diagnostic capability of skin cancer. The accurate diagnosis of skin cancer is challenging for dermatologists as multiple skin cancer types may appear similar in appearance. The dermatologists have an average accuracy of 62% to 80% in skin cancer diagnosis. The research community has been made significant progress in developing automated tools to assist dermatologists in decision making. In this work, we propose an automated computer-aided diagnosis system for multi-class skin (MCS) cancer classification with an exceptionally high accuracy. The proposed method outperformed both expert dermatologists and contemporary deep learning methods for MCS cancer classification. We performed fine-tuning over seven classes of HAM10000 dataset and conducted a comparative study to analyse the performance of five pre-trained convolutional neural networks (CNNs) and four ensemble models. The maximum accuracy of 93.20% for individual model amongst the set of models whereas maximum accuracy of 92.83% for ensemble model is reported in this paper. We propose use of ResNeXt101 for the MCS cancer classification owing to its optimized architecture and ability to gain higher accuracy.