Reduction of NIFTI files storage and compression to facilitate telemedicine services based on quantization hiding of downsampling approach

Elhadad, Ahmed; Jamjoom, Mona; Abulkasim, Hussein

doi:10.1038/s41598-024-54820-4

Cited by 2 publications

(1 citation statement)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To surmount these challenges and significantly augment the efficiency of the model within the unique context of the aquaculture industry, a holistic suite of technical enhancements has been carefully curated and implemented. These advancements span the introduction of an innovative downsampling technique designed [5][6][7][8] to preserve pivotal image features, a thorough optimization of the model's structural backbone and neck to facilitate superior feature processing capabilities, and the recalibration of loss functions [9][10][11] to more accurately reflect the distinctive attributes of images captured by drones. Furthermore, the establishment of branches capable of high-resolution detection enables the precise identification of minuscule objects.…”

Section: Introductionmentioning

confidence: 99%

Deploying a Computer Vision Model Based on YOLOv8 Suitable for Drones in the Tuna Fishing and Aquaculture Industry

Pham,

Han

2024

JMSE

View full text Add to dashboard Cite

In recent years, the global tuna fishing and aquaculture industry has encountered significant challenges in balancing operational efficiency with sustainable resource management. This study introduces an innovative approach utilizing an advanced computer vision model, PA-YOLOv8, specifically adapted for drones, to enhance the monitoring and management of tuna populations. PA-YOLOv8 leverages the capabilities of YOLOv8, a state-of-the-art object detection system known for its precision and speed, tailored to address the unique demands of aerial surveillance in marine environments. Through comprehensive modifications including downsampling techniques, feature fusion enhancements, and the integration of the Global Attention Module (GAM), the model significantly improves the detection accuracy of small and juvenile tuna within complex aquatic landscapes. Experimental results using the Tuna dataset from Roboflow demonstrate marked improvements in detection metrics such as precision, recall, and mean average precision (mAP), affirming the model’s effectiveness. This study underscores the potential of integrating cutting-edge technologies like UAVs and computer vision in promoting sustainable practices in the aquaculture sector, setting a new standard for technological applications in environmental and resource management. The advancements presented here provide a scalable and efficient solution for real-time monitoring, contributing to the long-term sustainability of marine ecosystems.

show abstract

Section: Introductionmentioning

confidence: 99%

Deploying a Computer Vision Model Based on YOLOv8 Suitable for Drones in the Tuna Fishing and Aquaculture Industry

Pham,

Han

2024

JMSE

View full text Add to dashboard Cite

show abstract

Using machine learning to develop a stacking ensemble learning model for the CT radiomics classification of brain metastases

Zhang,

Wang,

et al. 2024

Sci Rep

View full text Add to dashboard Cite

The objective of this study was to explore the potential of machine-learning techniques in the automatic identification and classification of brain metastases from a radiomic perspective, aiming to improve the accuracy of tumor volume assessment for radiotherapy. By using various machine-learning algorithms, including random forest, support vector machine, gradient boosting machine, XGBoost, decision tree, artificial neural network, k-nearest neighbors, LightGBM, and CatBoost algorithms, a stacking ensemble model was developed to classify gross tumor volume (GTV), brainstem, and normal brain tissue based on radiomic features. Multiple evaluation metrics, including the specificity, sensitivity, negative predictive value, positive predictive value, accuracy, Matthews correlation coefficient, and the Youden index, were used to assess the model’s performance. The stacked ensemble model integrated the strengths of the nine base models and consistently outperformed individual base models in classifying GTV (area under the curve [AUC] = 0.928), brainstem (AUC = 0.932), and normal brain tissue (AUC = 0.942). Among the base models, the support vector machine model demonstrated the best performance in the three classifications (AUC = 0.922, 0.909, and 0.928). The higher performance of the stacked ensemble model highlighted the low performance of other models, including the decision tree (AUC = 0.709, 0.706, 0.804) and k-nearest neighbors (AUC = 0.721, 0.663, 0.729) models in certain contexts, such as when faced with high-dimensional feature spaces. While machine learning shows significant promise in medical image analysis, relying solely on a single model may lead to suboptimal results. By combining the strengths of various algorithms, the stacking ensemble model offers a better solution for the classification of brain metastases based on radiomic features. Supplementary Information The online version contains supplementary material available at 10.1038/s41598-024-80210-x.

show abstract

Reduction of NIFTI files storage and compression to facilitate telemedicine services based on quantization hiding of downsampling approach

Cited by 2 publications

References 32 publications

Deploying a Computer Vision Model Based on YOLOv8 Suitable for Drones in the Tuna Fishing and Aquaculture Industry

Deploying a Computer Vision Model Based on YOLOv8 Suitable for Drones in the Tuna Fishing and Aquaculture Industry

Using machine learning to develop a stacking ensemble learning model for the CT radiomics classification of brain metastases

Contact Info

Product

Resources

About