Artificial Intelligence-Based Approach for Determination of Haematologic Diseases

Malaria and Thalassaemia are the life threatening diseases and the analysis of blood is a powerful diagnostic tool for the detection of them. Manual detection by the light microscopy is the most widely used technique for determination of malaria and possible thalassaemia. But it is a labour intensive repetitive task with time consuming. The aim of this study is to develop an automated blood image analysis system for the rapid and accurate determination of malaria, possible thalassaemia, and other abnormal red cell disorders. Two back propagation Artificial Neural Network models (3 layers and 4 layers) have been employed together with image analysis techniques to evaluate the accuracy of the classification in the recognition of medical image patterns associated with morphological features of erythrocytes in the blood. The three layers Artificial Neural Network (ANN) architecture has the best performance with an error of 2.74545e-005 and 86.54% correct recognition rate. The trained three layer ANN act as a final detection classifier to determine the haematologic disease. A medical consultation system has been jointly used with this system to provide consultation power. A questioning and answering dialog on the basis of patient history, physical examination and routine diagnostic test has been conducted in the medical consultation system with image analyzing result made by the trained ANN.