There has been a steady escalation in the impact of Artificial Intelligence (AI) on Healthcare along with an increasing amount of progress being made in this field. While many entities are working on the development of significant deep learning models for the diagnosis of brain-related diseases, identifying precise images needed for model training and inference tasks is limited due to variation in DICOM fields which use free text to define things like series description, sequence and

more »

... \cite{willemink_koszek_hardell_wu_fleischmann_harvey_folio_summers_rubin_lungren_2020}. Detecting the orientation of brain MR scans (Axial/Sagittal/Coronal) remains a challenge due to these variations caused by linguistic barriers, human errors and de-identification - essentially rendering the tags unreliable. In this work, we propose a deep learning model that identifies the orientation of brain MR scans with near perfect accuracy.