  The performance of image classification methodsheavily relies on the
high-quality annotations, which are noteasily affordable, particularly for
medical data. To alleviate thislimitation, in this study, we propose a weakly
supervised imageclassification method based on combination of
hand-craftedfeatures. We hypothesize that integration of these
hand-craftedfeatures alongside Long short-term memory (LSTM) classifiercan
reduce the adverse effects of weak labels in classificationaccuracy. Our
proposed algorithm is based on selecting theappropriate domain representations
of the data in Wavelet andDiscrete Cosine Transform (DCT) domains. This
informationis then fed into LSTM network to account for the sequentialnature of
the data. The proposed efficient, low dimensionalfeatures exploit the power of
shallow deep learning modelsto achieve higher performance with lower
computational cost.In order to show efficacy of the proposed strategy, we
haveexperimented classification of brain tumor grades and achievedthe state of
the art performance with the resolution of 256 x 256. We also conducted a
comprehensive set of experiments toanalyze the effect of each component on the
performance.
