  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.
