标题： 利用人工智能通过超声成像进行多囊卵巢综合征的自动分类 显示英文标题

标题： Leveraging AI for Automatic Classification of PCOS Using Ultrasound Imaging

摘要： AUTO-PCOS分类挑战旨在通过自动分类健康和不健康的超声图像帧，推进人工智能（AI）在识别多囊卵巢综合征（PCOS）方面的诊断能力。 本报告概述了我们构建强大AI流程的方法，利用InceptionV3架构进行迁移学习，以实现二分类的高准确性。 预处理步骤确保数据集在训练、验证和测试中得到优化，而像LIME和显著性图这样的可解释性方法提供了关于模型决策的有价值见解。 我们的方法在验证数据上实现了90.52%的准确率，精确率、召回率和F1分数指标均超过90%，证明了其有效性。 该项目强调了人工智能在医疗保健中的变革潜力，特别是在解决PCOS等诊断挑战方面。 讨论了关键发现、挑战和未来改进的建议，突出了创建可靠、可解释和可扩展的AI驱动医疗诊断工具的路径。 显示英文摘要

摘要： The AUTO-PCOS Classification Challenge seeks to advance the diagnostic capabilities of artificial intelligence (AI) in identifying Polycystic Ovary Syndrome (PCOS) through automated classification of healthy and unhealthy ultrasound frames. This report outlines our methodology for building a robust AI pipeline utilizing transfer learning with the InceptionV3 architecture to achieve high accuracy in binary classification. Preprocessing steps ensured the dataset was optimized for training, validation, and testing, while interpretability methods like LIME and saliency maps provided valuable insights into the model's decision-making. Our approach achieved an accuracy of 90.52%, with precision, recall, and F1-score metrics exceeding 90% on validation data, demonstrating its efficacy. The project underscores the transformative potential of AI in healthcare, particularly in addressing diagnostic challenges like PCOS. Key findings, challenges, and recommendations for future enhancements are discussed, highlighting the pathway for creating reliable, interpretable, and scalable AI-driven medical diagnostic tools.