用智能算法自动调优医学影像诊断CNN，省时又高效
Optimization of Convolutional Neural Network Hyperparameter for Medical Image Diagnosis using Metaheuristic Algorithms: A short Recent Review (2019-2022)
- 用元启发式算法自动搜索最佳超参数配置
- 减少人工试错，提升模型在医疗图像上的诊断性能
- 适合想提升CNN效率的医学影像研究者
卷积神经网络（CNN）已在多种疾病医学诊断中取得成功。然而，在众多可能的网络结构和超参数中找到最优配置仍是一大挑战。传统方法依赖人工手动调参，计算成本高，需反复试验才能获得理想结果，且高度依赖研究人员的经验。本文综述2019至2022年间利用元启发式优化算法优化CNN超参数的研究进展，重点分析多类优化方法在提升CNN性能中的应用，帮助研究人员更高效地确定超参数设置。
原文摘要 · Abstract (English)
Convolutional Neural Networks (CNNs) have been successfully utilized in the medical diagnosis of many illnesses. Nevertheless, identifying the optimal architecture and hyperparameters among the available possibilities might be a substantial challenge. Typically, CNN hyperparameter selection is performed manually. Nonetheless, this is a computationally costly procedure, as numerous rounds of hyperparameter settings must be evaluated to determine which produces the best results. Choosing the proper hyperparameter settings has always been a crucial and challenging task, as it depends on the researcher's knowledge and experience. This study will present work done in recent years on the usage of metaheuristic optimization algorithms in the CNN optimization process. It looks at a number of recent studies that focus on the use of optimization methods to optimize hyperparameters in order to find high-performing CNNs. This helps researchers figure out how to set hyperparameters efficiently.
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