Ship Classification in High-Resolution SAR Images Using Deep Learning of Small Datasets

Wang, Yuan‐Yuan; Wang, Chao; Zhang, Hong

doi:10.3390/s18092929

Cited by 93 publications

(44 citation statements)

References 33 publications

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“…Deep learning based methods are already being used in various fields [17][18][19][20][21]. Different authors have already proposed several biomedical image detection techniques.…”

Section: Related Workmentioning

confidence: 99%

Efficient Pneumonia Detection in Chest Xray Images Using Deep Transfer Learning

et al. 2020

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Pneumonia causes the death of around 700,000 children every year and affects 7% of the global population. Chest X-rays are primarily used for the diagnosis of this disease. However, even for a trained radiologist, it is a challenging task to examine chest X-rays. There is a need to improve the diagnosis accuracy. In this work, an efficient model for the detection of pneumonia trained on digital chest X-ray images is proposed, which could aid the radiologists in their decision making process. A novel approach based on a weighted classifier is introduced, which combines the weighted predictions from the state-of-the-art deep learning models such as ResNet18, Xception, InceptionV3, DenseNet121, and MobileNetV3 in an optimal way. This approach is a supervised learning approach in which the network predicts the result based on the quality of the dataset used. Transfer learning is used to fine-tune the deep learning models to obtain higher training and validation accuracy. Partial data augmentation techniques are employed to increase the training dataset in a balanced way. The proposed weighted classifier is able to outperform all the individual models. Finally, the model is evaluated, not only in terms of test accuracy, but also in the AUC score. The final proposed weighted classifier model is able to achieve a test accuracy of 98.43% and an AUC score of 99.76 on the unseen data from the Guangzhou Women and Children’s Medical Center pneumonia dataset. Hence, the proposed model can be used for a quick diagnosis of pneumonia and can aid the radiologists in the diagnosis process.

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“…Deep learning based methods are already being used in various fields [17][18][19][20][21]. Different authors have already proposed several biomedical image detection techniques.…”

Section: Related Workmentioning

confidence: 99%

Efficient Pneumonia Detection in Chest Xray Images Using Deep Transfer Learning

et al. 2020

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“…VGG-Nets are a set of very deep CNN that were initially proposed by Visual Geometry Group in ImageNet Large Scale Visual Recognition Competition (ILSVRC) 2014. They have been applied to other image analysis applications (Choi et al, 2017;Zhen et al, 2017;Wang et al, 2018). We adopted the architecture of VGG19, one of VGG-Nets models for our study.…”

Section: Pre-training In the Source Domainmentioning

confidence: 99%

Early Prediction of Cognitive Deficit in Very Preterm Infants Using Brain Structural Connectome With Transfer Learning Enhanced Deep Convolutional Neural Networks

Chen

Wang

et al. 2020

Front. Neurosci.

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Up to 40% of very preterm infants (≤32 weeks' gestational age) were identified with a cognitive deficit at 2 years of age. Yet, accurate clinical diagnosis of cognitive deficit cannot be made until early childhood around 3-5 years of age. Recently, brain structural connectome that was constructed by advanced diffusion tensor imaging (DTI) technique has been playing an important role in understanding human cognitive functions. However, available annotated neuroimaging datasets with clinical and outcome information are usually limited and expensive to enlarge in the very preterm infants' studies. These challenges hinder the development of neonatal prognostic tools for early prediction of cognitive deficit in very preterm infants. In this study, we considered the brain structural connectome as a 2D image and applied established deep convolutional neural networks to learn the spatial and topological information of the brain connectome. Furthermore, the transfer learning technique was utilized to mitigate the issue of insufficient training data. As such, we developed a transfer learning enhanced convolutional neural network (TL-CNN) model for early prediction of cognitive assessment at 2 years of age in very preterm infants using brain structural connectome. A total of 110 very preterm infants were enrolled in this work. Brain structural connectome was constructed using DTI images scanned at term-equivalent age. Bayley III cognitive assessments were conducted at 2 years of corrected age. We applied the proposed model to both cognitive deficit classification and continuous cognitive score prediction tasks. The results demonstrated that TL-CNN achieved improved performance compared to multiple peer models. Finally, we identified the brain regions most discriminative to the cognitive deficit. The results suggest that deep learning models may facilitate early prediction of later neurodevelopmental outcomes in very preterm infants at term-equivalent age.

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“…The number of convolution kernels in the fourth convolution layer was considered twice in the previous layer to facilitate improvement in the overall feature-extraction result. Wang et al [34] has proved that the number of neural units in the fully connected layer has As described in Figure 5 and Table 2, the proposed network comprises of four convolution layers, two max pooling layers, two fully connected layers, and the Softmax layer. After preprocessing (refer Section 3.1.3), the size of ship images equaled 56 pixels × 56 pixels.…”

Section: Improved Cnnmentioning

confidence: 99%

“…The number of convolution kernels in the fourth convolution layer was considered twice in the previous layer to facilitate improvement in the overall feature-extraction result. Wang et al [34] has proved that the number of neural units in the fully connected layer has little impact on the classification results. Therefore, the first fully connected layer (i.e., FC1) connects all feature maps generated by the fourth convolutional layer to obtain a 384-dimensional feature vector; likewise, the second fully connected layer (i.e., FC2) comprises of a 192-dimensional feature vector.…”

Section: Improved Cnnmentioning

confidence: 99%

Multi-Feature Fusion with Convolutional Neural Network for Ship Classification in Optical Images

2019

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The appearance of ships is easily affected by external factors—illumination, weather conditions, and sea state—that make ship classification a challenging task. To facilitate realization of enhanced ship-classification performance, this study proposes a ship classification method based on multi-feature fusion with a convolutional neural network (CNN). First, an improved CNN characterized by shallow layers and few parameters is proposed to learn high-level features and capture structural information. Second, handcrafted features of the histogram of oriented gradients (HOG) and local binary patterns (LBP) are combined with high-level features extracted by the improved CNN in the last fully connected layer to obtain discriminative feature representation. The handcrafted features supplement the edge information and spatial texture information of the ship images. Then, the Softmax function is used to classify different types of ships in the output layer. Effectiveness of the proposed method is evaluated based on its application to two datasets—one self-built and the other publicly available, called visible and infrared spectrums (VAIS). As observed, the proposed method demonstrated attainment of average classification accuracies equal to 97.50% and 93.60%, respectively, when applied to these datasets. Additionally, results obtained in terms of the F1-score and confusion matrix demonstrate the proposed method to be superior to some state-of-the-art methods.

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Ship Classification in High-Resolution SAR Images Using Deep Learning of Small Datasets

Cited by 93 publications

References 33 publications

Efficient Pneumonia Detection in Chest Xray Images Using Deep Transfer Learning

Efficient Pneumonia Detection in Chest Xray Images Using Deep Transfer Learning

Early Prediction of Cognitive Deficit in Very Preterm Infants Using Brain Structural Connectome With Transfer Learning Enhanced Deep Convolutional Neural Networks

Multi-Feature Fusion with Convolutional Neural Network for Ship Classification in Optical Images

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