A deep ensemble network model for classifying and predicting breast cancer

Breast cancer is one of the leading causes of death among women worldwide. In most cases, the misinterpretation of medical diagnosis plays a vital role in increased fatality rates due to breast cancer. Breast cancer can be diagnosed by classifying tumors. There are two different types of tumors, such as malignant and benign tumors. Identifying the type of tumor is a tedious task, even for experts. Hence, an automated diagnosis is necessary. The role of machine learning in medical diagnosis is eminent as it provides more accurate results in classifying and predicting diseases. In this paper, we propose a deep ensemble network (DEN) method for classifying and predicting breast cancer. This method uses a stacked convolutional neural network, artificial neural network and recurrent neural network as the base classifiers in the ensemble. The random forest algorithm is used as the meta-learner for providing the final prediction. Experimental results show that the proposed DEN technique outperforms all the existing approaches in terms of accuracy, sensitivity, specificity, F-score and area under the curve (AUC) measures. The analysis of variance test proves that the proposed DEN model is statistically more significant than the other existing classification models; thus, the proposed approach may aid in the early detection and diagnosis of breast cancer in women, hence aiding in the development of early treatment techniques to increase survival rate.     

A new convolutional neural network model for peripapillary atrophy area segmentation from retinal fundus images

Peripapillary atrophy (PPA) is a clinical finding, which reflects the atrophy of retina layer and retinal pigment epithelium. The size of PPA area is a useful medical indicator, as it is highly associated with many diseases such as glaucoma and myopia. Therefore, separating the PPA area from retinal images, which is called PPA area segmentation, is very important. It is a challenging task, because PPA areas are irregular and non-uniform, and their contours are blurry and change gradually. To solve these issues, we transform the PPA area segmentation task into a task of segmenting another two areas with relatively regular and uniform shapes, and then propose a novel multi-task fully convolutional network (MFCN) model to jointly extract them from retinal images. Meanwhile, we take edge continuity of the target area into consideration. To evaluate the performance of the proposed model, we conduct experiments on images with PPA areas labelled by experts and achieve an average precision of 0.8928, outperforming the state-of-the-art models. To demonstrate the application of PPA segmentation in medical research, we apply PPA related features based on the segmented PPA area on differentiating glaucomatous and physiologic large cup cases. Experiment conducted on real datasets confirms the effectiveness of using these features for glaucoma diagnosis.     

The skin cancer classification using deep convolutional neural network

Multimedia Tools and Applications - This paper addresses the demand for an intelligent and rapid classification system of skin cancer using contemporary highly-efficient deep convolutional neural...     

Fuzzy cluster based neural network classifier for classifying breast tumors in ultrasound images

The performance of supervised classification algorithms is highly dependent on the quality of training data. Ambiguous training patterns may misguide the classifier leading to poor classification performance. Further, the manual exploration of class labels is an expensive and time consuming process. An automatic method is needed to identify noisy samples in the training data to improve the decision making process. This article presents a new classification technique by combining an unsupervised learning technique (i.e. fuzzy c-means clustering (FCM)) and supervised learning technique (i.e. back-propagation artificial neural network (BPANN)) to categorize benign and malignant tumors in breast ultrasound images. Unsupervised learning is employed to identify ambiguous examples in the training data. Experiments were conducted on 178 B-mode breast ultrasound images containing 88 benign and 90 malignant cases on MATLAB® software platform. A total of 457 features were extracted from ultrasound images followed by feature selection to determine the most significant features. Accuracy, sensitivity, specificity, area under the receiver operating characteristic curve (AUC) and Mathew's correlation coefficient (MCC) were used to access the performance of different classifiers. The result shows that the proposed approach achieves classification accuracy of 95.862% when all the 457 features were used for classification. However, the accuracy is reduced to 94.138% when only 19 most relevant features selected by multi-criterion feature selection approach were used for classification. The results were discussed in light of some recently reported studies. The empirical results suggest that eliminating doubtful training examples can improve the decision making performance of expert systems. The proposed approach show promising results and need further evaluation in other applications of expert and intelligent systems.     

A convolutional neural network model for semantic segmentation of mitotic events in microscopy images

Neural Computing and Applications - Mitosis, which has important effects such as healing and growing for human body, has attracted considerable attention in recent years. Especially, cell division...     

Gland segmentation in colorectal cancer histopathological images using U-net inspired convolutional network

Neural Computing and Applications - The accurate gland segmentation from digitized H&E (hematoxylin and eosin) histology images with a wide range of histologic grades of cancer is quite...     

深度卷积神经网络的汽车车型识别方法

针对现有汽车车型识别方法计算量大、提取特征复杂等问题,提出一种基于深度卷积神经网络的汽车车型识别方法。该方法借助于深度学习,对经典的卷积神经网络做出改进并得到由多个卷积层和次抽样层构成的深度卷积神经网络。根据五种车型的分类结果,表明该方法在识别率方面较传统方法有明显的提高。实验还研究了网络层数、卷积核大小、特征维数对深度卷积神经网络的性能和识别率的影响。     

A transfer learning-based novel fusion convolutional neural network for breast cancer histology classification

Multimedia Tools and Applications - To train a convolutional neural network (CNN) from scratch is not suitable for medical image tasks with insufficient data. Benefiting from the transfer learning,...     

Pyramid-dilated deep convolutional neural network for crowd counting

Statistics on crowds in crowded scenes can reflect the density level of crowds and provide safety warnings. This is a laborious task if conducted manually. In recent years, automated crowd counting has received extensive attention in the computer vision field. However, this task is still challenging mainly due to the serious occlusion in crowds and large appearance variations caused by the viewing angles of cameras. To overcome these difficulties, a pyramid-dilated deep convolutional neural network for accurate crowd counting called PDD-CNN is proposed. PDD-CNN is based on a VGG-16 network that is designed to generate dense attribute feature maps from an image with an arbitrary size or resolution. Then, two pyramid dilated modules are adopted, each consisting of four parallel dilated convolutional layers with different rates and a parallel average pooling layer to capture the multiscale features. Finally, three cascading dilated convolutions are used to regress the density map and perform accurate count estimation. In addition, a novel training loss, combining the Euclidean loss with the structural similarity loss, is employed to attenuate the blurry effects of density map estimation. The experimental results on three datasets (ShanghaiTech, UCF_CC_50, and UCF-QNRF) demonstrate that the proposed PDD-CNN produces high-quality density maps and achieves a good counting performance.

     

Multi-task deep learning for fine-grained classification and grading in breast cancer histopathological images

Multimedia Tools and Applications - Fine-grained classification and grading of breast cancer (BC) histopathological images are of great value in clinical application. However, automatic...     

A hybrid deep learning model by combining convolutional neural network and recurrent neural network to detect forest fire

Multimedia Tools and Applications - Forest fire poses a serious threat to wildlife, environment, and all mankind. This threat has prompted the development of various intelligent and computer vision...     

Attention-based convolutional neural network for deep face recognition

Multimedia Tools and Applications - Discriminative feature embedding is of essential importance in the field of large scale face recognition. In this paper, we propose an attention-based...     

A dyadic multi-resolution deep convolutional neural wavelet network for image classification

For almost the past four decades, image classification has gained a lot of attention in the field of pattern recognition due to its application in various fields. Given its importance, several approaches have been proposed up to now. In this paper, we will present a dyadic multi-resolution deep convolutional neural wavelets’ network approach for image classification. This approach consists of performing the classification of one class versus all the other classes of the dataset by the reconstruction of a Deep Convolutional Neural Wavelet Network (DCNWN). This network is based on the Neural Network (NN) architecture, the Fast Wavelet Transform (FWT) and the Adaboost algorithm. It consists, first, of extracting features using the FWT based on the Multi-Resolution Analysis (MRA). These features are used to calculate the inputs of the hidden layer. Second, those inputs are filtered by using the Adaboost algorithm to select the best ones corresponding to each image. Third, we create an AutoEncoder (AE) using wavelet networks of all images. Finally, we apply a pooling for each hidden layer of the wavelet network to obtain a DCNWN that permits the classification of one class and rejects all other classes of the dataset. Classification rates given by our approach show a clear improvement compared to those cited in this article.

     

基于改进的深度神经网络的人体动作识别模型

针对现有人体动作识别方法需输入固定长度的视频段、未充分利用时空信息等问题,提出一种基于时空金字塔和注意力机制相结合的深度神经网络模型,将包含时空金字塔的3D-CNN和添加时空注意力机制的LSTM模型相结合,实现了对视频段的多尺度处理和对动作的复杂时空信息的充分利用。以RGB图像和光流场作为空域和时域的输入,以融合金字塔池化层的运动和外观特征后的融合特征作为融合域的输入,最后采用决策融合策略获得最终动作识别结果。在UCF101和HMDB51数据集上进行实验,分别取得了94.2%和70.5%的识别准确率。实验结果表明,改进的网络模型在基于视频的人体动作识别任务上获得了较高的识别准确率。     

用于肺炎图像分类的优化卷积神经网络方法

目前,卷积神经网络（CNN）开始应用在肺炎分类领域。针对层数较浅、结构较为简单的卷积网络对肺炎识别的准确率难以提高的情况,采用深度学习方法,并针对采用深度学习方法时常常需要消耗大量的系统资源,导致卷积网络难以在用户端部署的问题,提出一种使用优化的卷积神经网络的分类方法。首先,根据肺炎图像的特征,选择具有良好图像分类性能的AlexNet与InceptionV3模型;然后,利用医学影像特点对层次更深、结构更加复杂的InceptionV3模型进行预训练;最后,通过知识蒸馏的方法,将训练好的"知识"（有效信息）提取到AlexNet模型中,从而实现在减少系统资源占用的同时,提高准确率的效果。实验数据表明,使用知识蒸馏后,AlexNet模型的准确率、特异性与灵敏度分别提高了4.1、7.45、1.97个百分点,且对图像处理器（GPU）占用相比InceptionV3模型减小了51个百分点。     

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.     

DGCNN: deep convolutional generative adversarial network based convolutional neural network for diagnosis of COVID-19

Multimedia Tools and Applications - The latest threat to global health is the coronavirus disease 2019 (COVID-19) pandemic. To prevent COVID-19, recognizing and isolating the infected patients is...     

Saliency-based deep convolutional neural network for no-reference image quality assessment

In this paper, we proposed a novel method for No-Reference Image Quality Assessment (NR-IQA) by combining deep Convolutional Neural Network (CNN) with saliency map. We first investigate the effect of depth of CNNs for NR-IQA by comparing our proposed ten-layer Deep CNN (DCNN) for NR-IQA with the state-of-the-art CNN architecture proposed by Kang et al. (2014). Our results show that the DCNN architecture can deliver a higher accuracy on the LIVE dataset. To mimic human vision, we introduce saliency maps combining with CNN to propose a Saliency-based DCNN (SDCNN) framework for NR-IQA. We compute a saliency map for each image and both the map and the image are split into small patches. Each image patch is assigned with a patch importance value based on its saliency patch. A set of Salient Image Patches (SIPs) are selected according to their saliency and we only apply the model on those SIPs to predict the quality score for the whole image. Our experimental results show that the SDCNN framework is superior to other state-of-the-art approaches on the widely used LIVE dataset. The TID2008 and the CISQ image quality datasets are utilised to report cross-dataset results. The results indicate that our proposed SDCNN can generalise well on other datasets.     

Macro unit-based convolutional neural network for very light-weight deep learning

When implementing a deep neural network in an embedded system or SoC for mobile devices, its large parameter size can be a significant burden on the internal memory design. In this paper, we propose a new deep neural network that reduces computation and the number of model parameters but maintains reasonable performance. The configuration of the proposed network is as follows: First, we present a macro unit (MU) to reduce heavy computations and also to learn sufficient feature maps. Second, we employ asymmetric convolution of the well-known Inception network to further efficiently manipulate feature maps within the MU. Third, all the feature maps produced from MU(s) of each layer are concatenated and then the grouped feature map is distributed to all the MUs of the next layer for transferring richer information. Experimental results show that the proposed network achieves about 10% higher performance than DenseNet-BC in case of extremely small parameter size for CIFAR-100. The proposed network also has very few learning parameters and smaller floating point operations per second (FLOPS) than the other networks optimized for mobile devices such as MobileNet V2.     

改进深度残差卷积神经网络的LDCT图像估计

针对低剂量计算机断层扫描（Low-Dose Computed Tomography,LDCT）重建图像出现明显条形伪影的现象,提出了一种基于残差学习的深度卷积神经网络（Deep Residual Convolutional Neural Network,DR-CNN）模型,可以从LDCT图像预测标准剂量计算机断层扫描（Normal-Dose Computed Tomography,NDCT）图像。该模型在训练阶段,将数据集中的LDCT图像和NDCT图像相减得到残差图像,将LDCT图像和残差图像分别作为输入和标签,通过深度卷积神经网络（Convolution Neural Network,CNN）学习输入和标签之间的映射关系;在测试阶段,利用此映射关系从LDCT图像预测残差图像,用LDCT图像减去残差图像得到预测的NDCT图像。实验采用50对大小为512×512的同一体模的常规剂量胸腔扫描切片和投影域添加噪声后的重建图像作为数据集,其中45对作为训练集,其他作为测试集,来验证此模型的有效性。通过与非局部降噪算法、匹配三维滤波算法和K-SVD算法等目前公认效果较好的图像去噪算法对比,所提模型预测的NDCT图像均方根误差小,且信噪比略高于其他算法处理结果。