UNASSIGNED: Morphological identification of @entity3052 is a powerful tool used by hematologists to determine the family of such a disease. In some cases, experienced physicians are even able to determine the @entity1778 subtype of the sample. However, the identification process may have error rates up to 40% (when classifying @entity3052 subtypes) depending on the physician's experience and the sample quality. This problem raises the need to create automatic tools that provide hematologists with a second opinion during the classification process. Our research presents a contextual analysis methodology for the detection of @entity3052 subtypes from bone marrow cells images. We propose a cells separation algorithm to break up overlapped regions. In this phase, we achieved an average accuracy of 95% in the evaluation of the segmentation process. In a second phase, we extract descriptive features to the nucleus and cytoplasm obtained in the segmentation phase in order to classify @entity1778 families and subtypes. We finally created a decision algorithm that provides an automatic diagnosis for a @entity1 . In our experiments, we achieved an overall accuracy of 92% in the supervised classification of @entity3052 families, 84% for the lymphoblastic subtypes, and 92% for the myeloblastic subtypes. Finally, we achieved accuracies of 95% in the diagnosis of @entity1778 families and 90% in the diagnosis of @entity1778 subtypes.

Segmentation and Classification of Bone Marrow Cells Images Using Contextual Information for Medical Diagnosis of XXXX .

@entity1 :: ('9606', 'Species') :: ['patient'] @entity1778 :: ('MESH:D007938', 'Disease') :: ['leukemia'] @entity3052 :: ('MESH:D015470', 'Disease') :: ['acute leukemia']
@entity3052 :: (MESH:D015470,Disease) :: ['Acute Leukemias']
