复杂场景分类是遥感图像解译的一项重要内容。本文通过优化ResNet18深度残差网络和随机森林,实现了遥感图像复杂场景的高精度分类。首先通过数据扩充将数据库扩充以缓解因训练样本少带来的过拟合问题,然后采用ResNet18深度残差网络自动...复杂场景分类是遥感图像解译的一项重要内容。本文通过优化ResNet18深度残差网络和随机森林,实现了遥感图像复杂场景的高精度分类。首先通过数据扩充将数据库扩充以缓解因训练样本少带来的过拟合问题,然后采用ResNet18深度残差网络自动提取遥感图像场景特征,最后使用随机森林分类器实现复杂场景分类任务并分别在NWPU-RESISC45和UC Merced Land Use数据库上进行了实验。结果表明,本文模型场景分类准确率分别为98.86%和99.17%,与单独使用ResNet18深度残差网络相比,本文模型分类准确率分别提高3.36%和1.71%,相比于其他场景分类方法,本文模型分类准确率分别提高5.23%和1.55%。展开更多
The COVID-19 pandemic has had a widespread negative impact globally. It shares symptoms with other respiratory illnesses such as pneumonia and influenza, making rapid and accurate diagnosis essential to treat individu...The COVID-19 pandemic has had a widespread negative impact globally. It shares symptoms with other respiratory illnesses such as pneumonia and influenza, making rapid and accurate diagnosis essential to treat individuals and halt further transmission. X-ray imaging of the lungs is one of the most reliable diagnostic tools. Utilizing deep learning, we can train models to recognize the signs of infection, thus aiding in the identification of COVID-19 cases. For our project, we developed a deep learning model utilizing the ResNet50 architecture, pre-trained with ImageNet and CheXNet datasets. We tackled the challenge of an imbalanced dataset, the CoronaHack Chest X-Ray dataset provided by Kaggle, through both binary and multi-class classification approaches. Additionally, we evaluated the performance impact of using Focal loss versus Cross-entropy loss in our model.展开更多
In medical imaging, particularly for analyzing brain tumor MRIs, the expertise of skilled neurosurgeons or radiologists is often essential. However, many developing countries face a significant shortage of these speci...In medical imaging, particularly for analyzing brain tumor MRIs, the expertise of skilled neurosurgeons or radiologists is often essential. However, many developing countries face a significant shortage of these specialists, which impedes the accurate identification and analysis of tumors. This shortage exacerbates the challenge of delivering precise and timely diagnoses and delays the production of comprehensive MRI reports. Such delays can critically affect treatment outcomes, especially for conditions requiring immediate intervention, potentially leading to higher mortality rates. In this study, we introduced an adapted convolutional neural network designed to automate brain tumor diagnosis. Our model features fewer layers, each optimized with carefully selected hyperparameters. As a result, it significantly reduced both execution time and memory usage compared to other models. Specifically, its execution time was 10 times shorter than that of the referenced models, and its memory consumption was 3 times lower than that of ResNet. In terms of accuracy, our model outperformed all other architectures presented in the study, except for ResNet, which showed similar performance with an accuracy of around 90%.展开更多
文摘复杂场景分类是遥感图像解译的一项重要内容。本文通过优化ResNet18深度残差网络和随机森林,实现了遥感图像复杂场景的高精度分类。首先通过数据扩充将数据库扩充以缓解因训练样本少带来的过拟合问题,然后采用ResNet18深度残差网络自动提取遥感图像场景特征,最后使用随机森林分类器实现复杂场景分类任务并分别在NWPU-RESISC45和UC Merced Land Use数据库上进行了实验。结果表明,本文模型场景分类准确率分别为98.86%和99.17%,与单独使用ResNet18深度残差网络相比,本文模型分类准确率分别提高3.36%和1.71%,相比于其他场景分类方法,本文模型分类准确率分别提高5.23%和1.55%。
文摘The COVID-19 pandemic has had a widespread negative impact globally. It shares symptoms with other respiratory illnesses such as pneumonia and influenza, making rapid and accurate diagnosis essential to treat individuals and halt further transmission. X-ray imaging of the lungs is one of the most reliable diagnostic tools. Utilizing deep learning, we can train models to recognize the signs of infection, thus aiding in the identification of COVID-19 cases. For our project, we developed a deep learning model utilizing the ResNet50 architecture, pre-trained with ImageNet and CheXNet datasets. We tackled the challenge of an imbalanced dataset, the CoronaHack Chest X-Ray dataset provided by Kaggle, through both binary and multi-class classification approaches. Additionally, we evaluated the performance impact of using Focal loss versus Cross-entropy loss in our model.
文摘In medical imaging, particularly for analyzing brain tumor MRIs, the expertise of skilled neurosurgeons or radiologists is often essential. However, many developing countries face a significant shortage of these specialists, which impedes the accurate identification and analysis of tumors. This shortage exacerbates the challenge of delivering precise and timely diagnoses and delays the production of comprehensive MRI reports. Such delays can critically affect treatment outcomes, especially for conditions requiring immediate intervention, potentially leading to higher mortality rates. In this study, we introduced an adapted convolutional neural network designed to automate brain tumor diagnosis. Our model features fewer layers, each optimized with carefully selected hyperparameters. As a result, it significantly reduced both execution time and memory usage compared to other models. Specifically, its execution time was 10 times shorter than that of the referenced models, and its memory consumption was 3 times lower than that of ResNet. In terms of accuracy, our model outperformed all other architectures presented in the study, except for ResNet, which showed similar performance with an accuracy of around 90%.
