Engineering-oriented bridge multiple-damage detection with damage integrity using modified faster region-based convolutional neural network

Multimedia Tools and Applications - A bridge damage detector with preserving integrity based on modified Faster region-based convolutional neural network (R-CNN) is proposed for multiple damage...     

Quantum convolutional neural network for image classification

Pattern Analysis and Applications - In this paper we propose two scale-inspired local feature extraction methods based on Quantum Convolutional Neural Network (QCNN) in the Tensorflow quantum...     

CT image classification based on convolutional neural network

Neural Computing and Applications - With the rapid development of the Internet, image information is explosively growing. Traditional image classification methods are difficult to deal with huge...     

Bi-branch deconvolution-based convolutional neural network for image classification

With the rise of deep neural network, convolutional neural networks show superior performances on many different computer vision recognition tasks. The convolution is used as one of the most efficient ways for extracting the details features of an image, while the deconvolution is mostly used for semantic segmentation and significance detection to obtain the contour information of the image and rarely used for image classification. In this paper, we propose a novel network named bi-branch deconvolution-based convolutional neural network (BB-deconvNet), which is constructed by mainly stacking a proposed simple module named Zoom. The Zoom module has two branches to extract multi-scale features from the same feature map. Especially, the deconvolution is borrowed to one of the branches, which can provide distinct features differently from regular convolution through the zoom of learned feature maps. To verify the effectiveness of the proposed network, we conduct several experiments on three object classification benchmarks (CIFAR-10, CIFAR-100, SVHN). The BB-deconvNet shows encouraging performances compared with other state-of-the-art deep CNNs.     

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...     

Improved convolutional neural network in remote sensing image classification

Neural Computing and Applications - The classification of land cover is the first step in the analysis and application of remote sensing data in land resources. How to solve the multi-category...     

Multi-label convolutional neural network based pedestrian attribute classification

Recently, pedestrian attributes like gender, age, clothing etc., have been used as soft biometric traits for recognizing people. Unlike existing methods that assume the independence of attributes during their prediction, we propose a multi-label convolutional neural network (MLCNN) to predict multiple attributes together in a unified framework. Firstly, a pedestrian image is roughly divided into multiple overlapping body parts, which are simultaneously integrated in the multi-label convolutional neural network. Secondly, these parts are filtered independently and aggregated in the cost layer. The cost function is a combination of multiple binary attribute classification cost functions. Experiments show that the proposed method significantly outperforms the SVM based method on the PETA database.     

Hyperspectral image classification via parallel multi-input mechanism-based convolutional neural network

In recent years, Convolutional Neural Networks (CNNs) have succeeded in Hyperspectral Image Classification and shown excellent performance. However, the implicit spatial information between features, which significantly affect the classification performance of CNNs, are neglected in most existing CNN models. To address this issue, we propose a parallel multi-input mechanism-based CNN (PMI-CNN) fully exploiting the implicit spectral-spatial information in Hyperspectral Images. PMI-CNN employs four parallel convolution branches to extract spatial features with different levels, feature maps from each branch are spliced, and used as the classifier’s input. The proposed PMI-CNN’s classification performance is examined on three benchmark datasets and compared with six competing models. Experimental results show that PMI-CNN has better classification performance via exploiting spectral-spatial information. Compared with other models, the classification accuracy of PMI-CNN on the Indian Pines dataset is significantly improved, varying between 1.23%-25.36%. Likewise, the PMI-CNN, performed on the other two benchmark datasets, achieves 0.54%-12.26% and 0.96%-8.38% advantages in overall accuracy over the other six models, respectively.

     

Active weighted mapping-based residual convolutional neural network for image classification

Multimedia Tools and Applications - In visual recognition, the key to the performance improvement of ResNet is the success in establishing the stack of deep sequential convolutional layers using...     

A-optimal convolutional neural network

Neural Computing and Applications - In this paper, we propose a novel data representation-classification model learning algorithm. The model is a convolutional neural network (CNN), and we learn...     

Improved image classification with 4D light-field and interleaved convolutional neural network

Multimedia Tools and Applications - Image classification is a well-studied problem. However, there remains challenges for some special categories of images. This paper proposes a new deep...     

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.

     

Brain tumor classification using deep convolutional autoencoder-based neural network: multi-task approach

Diagnosis, detection and classification of tumors, in the brain MRI images, are important because misdiagnosis can lead to death. This paper proposes a method that can diagnose brain tumors in the MRI images and classify them into 5 categories using a Convolutional Neural Network (CNN). The proposed network uses a Convolutional Auto-Encoder Neural Network (CANN) to extract and learn deep features of input images. Extracted deep features from each level are combined to make desirable features and improve results. To classify brain tumor into three categories (Meningioma, Glioma, and Pituitary) the proposed method was applied on Cheng dataset and has reached a considerable performance accuracy of 99.3%. To diagnosis and grading Glioma tumors, the proposed method was applied on IXI and BraTS 2017 datasets, and to classify brain images into six classes including Meningioma, Pituitary, Astrocytoma, High-Grade Glioma, Low-Grade Glioma and Normal images (No tumor), the all datasets including IXI, BraTS2017, Cheng and Hazrat-e-Rassol, was used by the proposed network, and it has reached desirable performance accuracy of 99.1% and 98.5%, respectively.

     

Enhanced brain tumor classification using an optimized multi-layered convolutional neural network architecture

Multimedia Tools and Applications - Detecting and classifying a brain tumor is a challenge that consumes a radiologist’s time and effort while requiring professional expertise. To resolve...     

RETRACTED ARTICLE: Research on image classification method based on convolutional neural network

Neural Computing and Applications - Image classification method is currently the more popular image technology, but it still has certain problems in practice. In order to improve the image...     

Correction to: Active weighted mapping-based residual convolutional neural network for image classification

Multimedia Tools and Applications -     

Hyperspectral remote sensing image classification based on dense residual three-dimensional convolutional neural network

Multimedia Tools and Applications - Data-driven deep learning techniques set the current state of the art in image classification for hyperspectral remote sensing images. The lack of labeled...     

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,...     

Examination of the ECG signal classification technique DEA-ELM using deep convolutional neural network features

Multimedia Tools and Applications - The accurate separation of ECG signals has become crucial to identify heart diseases. Machine learning methods are widely used to separate ECG signals. The aim...