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4th international conference on optimization, learning algorithms and applications, ol2a 2024

4th International Conference on Optimization, Learning Algor...

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4th international conference on optimization, learning algorithms and applications, ol2a 2024

ISBN: (纸本)9783031774317

the proceedings contain 41 papers. the special focus in this conference is on optimization, learning algorithms and applications. the topics include: Facial Expression Recognition in Virtual Reality Simulations;augmented Reality in Industrial Training: A Comparative Analysis of Teaching Methods;comparison Between Single and Multi-objective Clustering algorithms: MathE Case Study;learning algorithms for Breast Cancer Classification and Diagnosis;fuzzy c-Means as a Decision Support Tool for Liver Disease Diagnosis Based on Data Analysis;optimization of Machine learning Models Applied to Robot Localization in the RobotAtFactory 4.0 Competition;p-Fuzzy System Applied to Population Dynamics: A Case Study;enhancing K-Way Circuit Partitioning: A Deep Reinforcement learning Methodology;optimizing olive Disease Classification through Hybrid Machine learning and Deep learning Techniques;a Neural Network-Based Approach to Identifying Wrinkles and Recommending Cosmetic Products;colorectal Polyp Segmentation: Impact of Combining Different Datasets on Deep learning Model Performance;A Comparative Analysis of MATLAB and Python Neural Networks for Diabetes Prediction;cochineal Colony Detection in Cactus Pear: A Deep learning Approach;enhancing Quadruped Robot Performance through Gait optimization;assessment of Logistics Performance Indicators in Southern European Countries Optimizing the Decision Making;Predicting Retail Store Transaction Patterns: A Comparison of ARIMA and Machine learning Models;route optimization for Urban Last-Mile Delivery: Truck vs. Drone Performance;speeding up Line Search for Composite Objective Function with a Linear Inside Part;the Infodemic Issue: Numerical Modelling;a Multi-objective Approach for Solving Distributed Job Shop Scheduling Problems.

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4th international conference on optimization, learning algorithms and applications, ol2a 2024

4th International Conference on Optimization, Learning Algor...

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4th international conference on optimization, learning algorithms and applications, ol2a 2024

ISBN: (纸本)9783031774256

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Deep learning-Based Hip Detection in Pelvic Radiographs 3rd

Deep Learning-Based Hip Detection in Pelvic Radiographs

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3rd international conference on optimization, learning algorithms and applications (ol2a)

作者： Loureiro, Catia Filipe, Vitor Franco-Goncalo, Pedro Pereira, Ana Ines Colaco, Bruno Alves-Pimenta, Sofia Ginja, Mario Goncalves, Lio Univ Tras os Montes & Alto Douro UTAD Dept Engn P-5000801 Vila Real Portugal Inst Engn & Comp Syst Technol & Sci INESC TEC P-4200465 Porto Portugal Univ Tras os Montes & Alto Douro UTAD Dept Vet Sci P-5000801 Vila Real Portugal Univ Tras os Montes & Alto Douro UTAD Anim & Vet Res Ctr CECAV P-5000801 Vila Real Portugal Associate Lab Anim & Vet Sci AL4AnimalS P-5000801 Vila Real Portugal Univ Tras os Montes & Alto Douro UTAD Dept Anim Sci P-5000801 Vila Real Portugal

ISBN: (纸本)9783031530357;9783031530364

Radiography is the primary modality for diagnosing canine hip dysplasia (CHD), with visual assessment of radiographic features sometimes used for accurate diagnosis. However, these features typically constitute small regions of interest (ROI) within the overall image, yet they hold vital diagnostic information and are crucial for pathological analysis. Consequently, automated detection of ROIs becomes a critical preprocessing step in classification or segmentation systems. By correctly extracting the ROIs, the efficiency of retrieval and identification of pathological signs can be significantly improved. In this research study, we employed the most recent iteration of the YolO (version 8) model to detect hip joints in a dataset of 133 pelvic radiographs. the best-performing model achieved a mean average precision (mAP50:95) of 0.81, indicating highly accurate detection of hip regions. Importantly, this model displayed feasibility for training on a relatively small dataset and exhibited promising potential for various medical applications.

关键词： Deep learning Hip detection Canine hip dysplasia

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