This study aims to evaluate the performance of deep learning models in
predicting $\geq$M-class solar flares with a prediction window of 24 hours,
using hourly sampled full-disk line-of-sight (LoS) magnetogram images,
particularly focusing on the often overlooked flare events corresponding to the
near-limb regions (beyond $\pm$70$^{\circ}$ of the solar disk). We trained
three well-known deep learning architectures--AlexNet, VGG16, and ResNet34
using transfer learning and compared and evaluated the overall performance of
our models using true skill statistics (TSS) and Heidke skill score (HSS) and
computed recall scores to understand the prediction sensitivity in central and
near-limb regions for both X- and M-class flares. The following points
summarize the key findings of our study: (1) The highest overall performance
was observed with the AlexNet-based model, which achieved an average
TSS$\sim$0.53 and HSS$\sim$0.37; (2) Further, a spatial analysis of recall
scores disclosed that for the near-limb events, the VGG16- and ResNet34-based
models exhibited superior prediction sensitivity. The best results, however,
were seen with the ResNet34-based model for the near-limb flares, where the
average recall was approximately 0.59 (the recall for X- and M-class was 0.81
and 0.56 respectively) and (3) Our research findings demonstrate that our
models are capable of discerning complex spatial patterns from full-disk
magnetograms and exhibit skill in predicting solar flares, even in the vicinity
of near-limb regions. This ability holds substantial importance for operational
flare forecasting systems.

通过使用迁移学习训练深度学习模型并基于真实技能统计（TSS）、Heidke 评分（HSS）和召回得分，评估这项研究在预测具有 24 小时预测窗口的 M 级太阳耀斑方面的性能，特别关注于常常被忽视的近边区域（太阳盘的 ±70° 之外）的耀斑事件。研究发现，AlexNet 模型表现最佳，平均 TSS 约 0.53，平均 HSS 约 0.37；针对近边事件，VGG16 和 ResNet34 模型表现出较高的预测灵敏度，其中 ResNet34 模型在近边耀斑方面取得最佳结果，平均召回率约为 0.59（X 级和 M 级的召回率分别为 0.81 和 0.56）。此研究结果表明，模型能够从全盘磁图识别复杂的空间模式，并在近边区域预测太阳耀斑，对于运行中的耀斑预测系统具有重要意义。