Deep learning (DL) has emerged as a powerful image processing technique that learns the features of the data and produces state-of-the-art prediction results. The decade from 2010 to 2020 is a real revival of DL, which has come to a turning point in history. In image classification, many deep learning networks have been proposed by scholars, and each of them has its own strengthness. It is very important and efficient for the researchers and the developers to know the performance of these networks, especially for the beginners, so as to give them a transplant instruction by an objective evaluation index. In this paper, we constructed different data sets from three aspects, texture, shape, and measurement scale to test the performance of nine mainstream image classification networks. Cross-contrast experiments were performed to analyze the sensitivity of factors which influence the stability of image classification networks. Experimental results shown that in the 27 image datasets generated by the three image factors, the classification performance of AlexNet, GoogleNet, VggNet, and DenseNet is better. The perfomance comparison of these networks are showed and discussed in details. Code and pretrained models are available at https://github.com/liqiang12689/image-classification-finall .

/pdf/performance-evaluation-of-deep-learning-classification-3nyuwfov1t.pdf

Performance Evaluation of Deep Learning Classification Network for Image Features

Deep learning-based action classification technology has been applied to various fields, such as social safety, medical services, and sports.Analyzing an action on a practical level requires tracking multiple human bodies in an image in real-time and simultaneously classifying their actions.There are various related studies on the real-time classification of actions in an image.However, existing deep learning-based action classification models have prolonged response speeds, so there is a limit to real-time analysis.In addition, it has low accuracy of action of each object if multiple objects appear in the image.Also, it needs to be improved since it has a memory overhead in processing image data.Deep learning-based action classification using one-shot object detection is proposed to overcome the limitations of multiframe-based analysis technology.The proposed method uses a one-shot object detection model and a multi-object tracking algorithm to detect and track multiple objects in the image.Then, a deep learning-based pattern classification model is used to classify the body action of the object in the image by reducing the data for each object to an action vector.Compared to the existing studies, the constructed model shows higher accuracy of 74.95%, and in terms of speed, it offered better performance than the current studies at 0.234 s per frame.The proposed model makes it possible to classify some actions only through action vector learning without additional image learning because of the vector learning feature of the posterior neural network.Therefore, it is expected to contribute significantly to commercializing realistic streaming data analysis technologies, such as CCTV. 

Deep Learning-Based Action Classification Using One-Shot Object Detection

Semi-supervised lung nodule detection with adversarial learning

Image classification has been applied in many fields, which is an important branch of computer vision and pattern recognition. The boosting algorithm which is belong to ensemble learning can integrate several homogeneous classifiers, and combine the output layer’s result of every classifier to improve the final classification accuracy. In this paper, the Adaboost-DBN algorithm is used to combine the four weak classifiers (DBN) and construct a strong classifier. The Adaboost-DBN algorithm is based on the Adaboost M1 algorithm and is used to achieve higher classification accuracy. The proposed algorithm is tested on the Corel-1K data set, and the result of classification is significantly improved comparing to other classifiers.

Research on Image Classification Method Based on Adaboost-DBN

A pulmonary nodule classification method of Computer Tomography (CT) images based on transfer learning of deep convolutional neural network (CNN) is proposed. Lung CT images with labels are quite limited compared with the large scale image database such as ImageNet. It is easy to produce over-fitting problem when using the limited data to train the deep CNN for classification task. In this paper, in order to overcome this difficulty, the deep CNNs GoogleNet and ResNet are pre-trained on the large scale database ImageNet. The fully connected layers and the classifiers of the pre-trained networks are replaced to complete the classification of CT images of pulmonary nodules. A sub set of the Lung Image Database Consortium image collection (LIDC-IDRI) is used to fine-tune the network and validate the classification accuracy. This is the process of transfer learning. It solves the problem of the deficiency of lung CT images as labeled training data for CNNs. By the knowledge obtained from the pre-trained CNNs which have been trained on ImageNet, the network is easier to converge and the training time is greatly reduced. The classification accuracy of Pulmonary Nodules can be reached up to 71.88% by using the proposed method.

A Transfer Learning Method for CT Image Classification of Pulmonary Nodules

Diabetes is a chronic disease that seriously endangers human health, which should be early detection, early diagnosis and early treatment by establishing prediction model. With the help of disease auxiliary diagnosis based on machine learning, the process of early diagnosis could be more reliable. Then, the patients have more chances of early treatment. Deep learning technology can take advantage of its own powerful feature learning ability to the application of disease auxiliary diagnosis, and has gained good results. This paper proposes a diabetes prediction model based on Deep Belief Network (DBN). The model is established by using Pima Indians Diabetes data set, combined with cross-validation, setting DBN structure and adjusting DBN network parameters. The experimental results show that the accuracy of the model is as high as 77.60% and the performance is good.

Research on Diabetes Aided Diagnosis Model Based on Deep Belief Network

The invention discloses a pulmonary nodule texture feature extraction system and method based on a body local direction three-valued mode. The invention relates to a method for identifying pulmonary nodules from pulmonary CT images. An existing pulmonary nodule grammar feature extraction method has the problem of pulmonary nodule recognition rate caused by loss of partial texture information of animage. The pulmonary nodule texture feature extraction system and method based on a three-valued mode in a body local direction comprises: local mode extraction is carried out on adjacent slices of apulmonary nodule based on a VLBP mode; calculating a local adaptive threshold value of the extracted local mode by using a normal distribution function; performing three-valued probability statisticson the local three-valued mode in each direction by taking a central pixel as a center; and taking the three-valued probability in each direction obtained by statistics as a feature vector and a KNNclassification algorithm to identify the pulmonary nodules, and evaluating the pulmonary nodule texture feature identification result to obtain whether the identification result is correct or not. According to the method, the pulmonary nodule recognition accuracy is improved.

Pulmonary nodule texture feature extraction system and method based on body local direction three-valued mode

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Research on Image Classification Method Based on Adaboost-DBN

A Transfer Learning Method for CT Image Classification of Pulmonary Nodules

Research on Diabetes Aided Diagnosis Model Based on Deep Belief Network

Pulmonary nodule texture feature extraction system and method based on body local direction three-valued mode