  Solar flares, especially C, M, and X class, pose significant risks to
satellite operations, communication systems, and power grids. We present a
novel approach for predicting extreme solar flares using HMI intensitygrams and
magnetograms. By detecting sunspots from intensitygrams and extracting magnetic
field patches from magnetograms, we train a Residual Network (ResNet) to
classify extreme class flares. Our model demonstrates high accuracy, offering a
robust tool for predicting extreme solar flares and improving space weather
forecasting. Additionally, we show that HMI magnetograms provide more useful
data for deep learning compared to other SDO AIA images by better capturing
features critical for predicting flare magnitudes. This study underscores the
importance of identifying magnetic fields in solar flare prediction, marking a
significant advancement in solar activity prediction with practical
implications for mitigating space weather impacts.
