An Automated Differential Blood Count System

Abstract

While the early diagnosis of hematopoietic system disorders is view important in hematology, it is a highly complex and time consuming task. The early diagnosis requires a lot of patients to be followed-up by experts which, in general is in-feasible because of the required number of experts. The differential blood counter (DBC) system that we have developed is an attempt to automate the task performed manually by experts in routine. In our system, the cells are segmented using active contour models (snakes and ballons), which are initialized using morphological operators. Shape based and texture based features are utilized for the classification task. Different classifiers such as k-nearest neighbors, learning vector quantization, multi-layer perceptron and support vector machine are employed.

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Document Details

Document Type
Technical Report
Publication Date
Oct 25, 2001
Accession Number
ADA412558

Entities

People

  • Guclu Ongun
  • Kemal Leblebicioglu
  • Meral Beksac
  • Ugur Halici
  • Volkan Atalay

Organizations

  • Middle East Technical University

Tags

DTIC Thesaurus Topics

  • Abstracts
  • Artificial Intelligence Computing
  • Artificial Intelligence Software
  • Blood
  • Blood Counts
  • Classification
  • Computer Vision
  • Computers
  • Diseases And Disorders
  • Engineering
  • Hematopoietic System
  • Machine Learning
  • Middle East
  • Military Research
  • Neural Networks
  • Supervised Machine Learning

Fields of Study

  • Computer science

Readers

  • Computer Vision.
  • Mental Health of Military Veterans with Posttraumatic Stress Disorder (PTSD): Risk Factors, Prevalence, Symptoms, and Treatment.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks