The newly identified Coronavirus pneumonia, subsequently termed COVID-19, is highly transmittable and pathogenic with no clinically approved antiviral drug or vaccine available for treatment. The most common symptoms of COVID-19 are dry cough, sore throat, and fever. Symptoms can progress to a severe form of pneumonia with critical complications, including septic shock, pulmonary edema, acute respiratory distress syndrome and multi-organ failure. While medical imaging is not currently recommended in Canada for primary diagnosis of COVID-19, computer-aided diagnosis systems could assist in the early detection of COVID-19 abnormalities and help to monitor the progression of the disease, potentially reduce mortality rates. In this study, we compare popular deep learning-based feature extraction frameworks for automatic COVID-19 classification. To obtain the most accurate feature, which is an essential component of learning, MobileNet, DenseNet, Xception, ResNet, InceptionV3, InceptionResNetV2, VGGNet, NASNet were chosen amongst a pool of deep convolutional neural networks. The extracted features were then fed into several machine learning classifiers to classify subjects as either a case of COVID-19 or a control. This approach avoided task-specific data pre-processing methods to support a better generalization ability for unseen data. The performance of the proposed method was validated on a publicly available COVID-19 dataset of chest X-ray and CT images. The DenseNet121 feature extractor with Bagging tree classifier achieved the best performance with 99% classification accuracy. The second-best learner was a hybrid of the a ResNet50 feature extractor trained by LightGBM with an accuracy of 98%.

Automatic Detection of Coronavirus Disease (COVID-19) in X-ray and CT Images: A Machine Learning-Based Approach

Automatic Detection of Coronavirus Disease (COVID-19) in X-ray and CT Images: A Machine Learning Based Approach.

The classification of gliomas based on a Pyramid dilated convolution resnet model

The scene classification of high spatial resolution (HSR) images is a challenging task in the remote sensing community. How to construct a discriminative representation of the HSR scene is a key step to improve classification performance. In this letter, we propose a novel feature extraction method termed multilayer feature fusion network (MF2Net) for scene classification. At first, the transferred VGGNet-16 model is employed as a feature extractor to acquire multilayer convolutional features. Then, several layers including pooling, transformation, and fusion layers are designed to process hierarchical features in four branches, and the prediction probability can be obtained for classification. Finally, the proposed model is optimized by fine-tuning techniques, where a novel data augmentation approach is explored to improve generalization ability. As a result, MF2Net effectively applies useful information from multilayers to improve the accuracy of scene classification. The experimental results on AID and NWPU-RESISC45 data sets exhibit that the MF2Net method obtains quite competitive classification results compared with many state-of-the-art methods.

Multilayer Feature Fusion Network for Scene Classification in Remote Sensing

Self-driving cars are a hot research topic in science and technology, which has a great influence on social and economic development. Deep learning is one of the current key areas in the field of artificial intelligence research. It has been widely applied in image processing, natural language understanding, and so on. In recent years, more and more deep learning-based solutions have been presented in the field of self-driving cars and have achieved outstanding results. This paper presents a review of recent research on theories and applications of deep learning for self-driving cars. This survey provides a detailed explanation of the developments of self-driving cars and summarizes the applications of deep learning methods in the field of self-driving cars. Then the main problems in self-driving cars and their solutions based on deep learning methods are analyzed, such as obstacle detection, scene recognition, lane detection, navigation and path planning. In addition, the details of some representative approaches for self-driving cars using deep learning methods are summarized. Finally, the future challenges in the applications of deep learning for self-driving cars are given out.

A Survey on Theories and Applications for Self-Driving Cars Based on Deep Learning Methods

A novel scene classification model combining ResNet based transfer learning and data augmentation with a filter

In the unstructured family environment, robots are expected to provide various services to improve the quality of human life, based on the performance of specific action sequences generated by service planning. This paper focuses on one of the greatest challenges in service planning that is aimed at accomplishing the service tasks by generating appropriate object sequences to guide the robot on searching the corresponding target objects efficiently and reasonably. A well-structured knowledge-based framework of object search is proposed in our approach as well as taking into account the multi-domain knowledge of applying object, scene, and service in design. In order to improve the searching efficiency and reasonability, an ontology-based hierarchical and interrelated knowledge structure is formed to support the implementation of complicated service planning with either single or multiple tasks. The proposed framework is tested by comprehensive experiments, and the performance is evaluated with other mainstream methods in both simulation and real-world environments. The experimental results demonstrate the feasibility and effectiveness of applying this knowledge-based framework to efficient object searching aspect in service planning.

Service planning oriented efficient object search: A knowledge-based framework for home service robot

In this article, we presents an effective semantic grounding scheme to long-term mobile robots for dynamic object search in open/dynamic home environments. The challenge of this strategy lies in dealing with situations where the robot suffers the home environment with an unknown layout of the object and the target object is moved without the robot’s knowledge. Therefore, a general probabilistic model and a semantic model are established by investigating typical spatial location relations between object and room type, in order to guide the robot to prioritize the search effort for spaces that are most promising to find the target object. Furthermore, a semantic grounding solution is proposed to address the mutual independence between objects, endowing the robot with inference capability for dynamic object search. Also, our proposal can be shared with other robots that have not explored the environment in order to perform object search tasks. The presented scheme is fully evaluated by extensive experiments in the real-world environment. The results demonstrate the validity of our approach by comparing it with the other three schemes in terms of search time and trajectory length, and show that the proposed method allows the mobile robot to efficiently and robustly find the dynamic object while achieving the human-like performance.

Semantic Grounding for Long-Term Autonomy of Mobile Robots Toward Dynamic Object Search in Home Environments

A semantic robotic grasping framework based on multi-task learning in stacking scenes

This paper focuses on the problem of active object detection (AOD). AOD is important for service robots to complete tasks in the family environment, and leads robots to approach the target object by taking appropriate moving actions. Most of the current AOD methods are based on reinforcement learning with low training efficiency and testing accuracy. Therefore, an AOD model based on a deep Q-learning network (DQN) with a novel training algorithm is proposed in this paper. The DQN model is designed to fit the Q-values of various actions, and includes state space, feature extraction, and a multilayer perceptron. In contrast to existing research, a novel training algorithm based on memory is designed for the proposed DQN model to improve training efficiency and testing accuracy. In addition, a method of generating the end state is presented to judge when to stop the AOD task during the training process. Sufficient comparison experiments and ablation studies are performed based on an AOD dataset, proving that the presented method has better performance than the comparable methods and that the proposed training algorithm is more effective than the raw training algorithm.

Shaopeng Liu

Papers

A novel scene classification model combining ResNet based transfer learning and data augmentation with a filter

Service planning oriented efficient object search: A knowledge-based framework for home service robot

Semantic Grounding for Long-Term Autonomy of Mobile Robots Toward Dynamic Object Search in Home Environments

A semantic robotic grasping framework based on multi-task learning in stacking scenes

A deep Q-learning network based active object detection model with a novel training algorithm for service robots