Bone Marrow Cytomorphology Cell Detection using InceptionResNetV2
This provides a potential tool for haematology diagnostics, though it appears incremental as it applies an existing method to a specific medical domain.
The paper tackled the problem of automating bone marrow cell detection to address delays and variability in manual cytology, achieving 96.19% accuracy with a transfer learning model.
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.