  A chest X-ray is one of the most widely available radiological examinations
for diagnosing and detecting various lung illnesses. The National Institutes of
Health (NIH) provides an extensive database, ChestX-ray8 and ChestXray14, to
help establish a deep learning community for analysing and predicting lung
diseases. ChestX-ray14 consists of 112,120 frontal-view X-ray images of 30,805
distinct patients with text-mined fourteen disease image labels, where each
image has multiple labels and has been utilised in numerous research in the
past. To our current knowledge, no previous study has investigated outliers and
multi-label impact for a single X-ray image during the preprocessing stage. The
effect of outliers is mitigated in this paper by our proposed auto-outlier
fusion technique. The image label is regenerated by concentrating on a
particular factor in one image. The final cleaned dataset will be used to
compare the mechanisms of multi-head self-attention and multi-head attention
with generalised max-pooling.
