  Critical clinical decision points in haematology are influenced by the
requirement of bone marrow cytology for a haematological diagnosis. Bone marrow
cytology, however, is restricted to reference facilities with expertise, and
linked to inter-observer variability which requires a long time to process that
could result in a delayed or inaccurate diagnosis, leaving an unmet need for
cutting-edge supporting technologies. This paper presents a novel transfer
learning model for Bone Marrow Cell Detection to provide a solution to all the
difficulties faced for the task along with considerable accuracy. The proposed
model achieved 96.19\% accuracy which can be used in the future for analysis of
other medical images in this domain.
