Search Results (2)

Accurate glioma categorization using magnetic resonance (MR) imaging is critical for optimal treatment planning. However, the uneven and diffuse nature of glioma borders makes manual classification difficult and time-consuming. To address these limitations, we provide a unique strategy that combines image decomposition and local texture feature extraction to improve classification precision. The procedure starts with a Gaussian filter (GF) to smooth and reduce noise in MR images, followed by non-subsampled Laplacian Pyramid (NSLP) decomposition to capture multi-scale image information, making glioma borders more visible, TV-L1 normalization to handle intensity discrepancies, and local binary patterns (LBPs) to extract significant texture features from the processed images, which are then fed into a range of supervised machine learning classifiers, such as support vector machines (SVMs), K-nearest neighbors (KNNs), decision trees (DTs), AdaBoost, and LogitBoost, which have been trained to distinguish between low-grade (LG) and high-grade (HG) gliomas. According to experimental findings, our proposed approach consistently performs better than the state-of-the-art glioma classification techniques, with a higher degree of accuracy in differentiating LG and HG gliomas. This method has the potential to significantly increase diagnostic precision, enabling doctors to make better-informed and efficient treatment choices. Full article

Synthetic Aperture Radar (SAR) has become one of the important technical means of marine monitoring in the field of remote sensing due to its all-day, all-weather advantage. National territorial waters to achieve ship monitoring is conducive to national maritime law enforcement, implementation of maritime traffic control, and maintenance of national maritime security, so ship detection has been a hot spot and focus of research. After the development from traditional detection methods to deep learning combined methods, most of the research always based on the evolving Graphics Processing Unit (GPU) computing power to propose more complex and computationally intensive strategies, while in the process of transplanting optical image detection ignored the low signal-to-noise ratio, low resolution, single-channel and other characteristics brought by the SAR image imaging principle. Constantly pursuing detection accuracy while ignoring the detection speed and the ultimate application of the algorithm, almost all algorithms rely on powerful clustered desktop GPUs, which cannot be implemented on the frontline of marine monitoring to cope with the changing realities. To address these issues, this paper proposes a multi-channel fusion SAR image processing method that makes full use of image information and the network’s ability to extract features; it is also based on the latest You Only Look Once version 4 (YOLO-V4) deep learning framework for modeling architecture and training models. The YOLO-V4-light network was tailored for real-time and implementation, significantly reducing the model size, detection time, number of computational parameters, and memory consumption, and refining the network for three-channel images to compensate for the loss of accuracy due to light-weighting. The test experiments were completed entirely on a portable computer and achieved an Average Precision (AP) of 90.37% on the SAR Ship Detection Dataset (SSDD), simplifying the model while ensuring a lead over most existing methods. The YOLO-V4-lightship detection algorithm proposed in this paper has great practical application in maritime safety monitoring and emergency rescue. Full article