Classification of Acute Leukemia Based on Multilayer Perceptron

Link to publisher's homepage at https://iopscience.iop.org/In general, various artificial neural network have been applied in many areas such as modelling, pattern recognition, signal processing, diagnostic and prognostic. In this paper, artificial neural network are used to detect and classify the white blood cell (WBC) inside the acute leukemia blood samples. There are 25 features have been extracted from segmented WBC, which consist of shape, color and texture based features. Then, it have been fed up as the neural network inputs for the classification process in order to classify the segmented regions into two classes either B or T. The training algorithm for MLP network is LevenbergMarquardt (LM). The MLP network achieves the highest testing accuracy of 96.99% for 4 hidden nodes at state of 5 by using the overall 25 input features. Thus, MLP network trained by using LM algorithm is suitable for acute leukemia cells detection in blood sample

Classification of Acute Leukemia Based on Multilayer Perceptron

Abstract In general, various artificial neural network have been applied in many areas such as modelling, pattern recognition, signal processing, diagnostic and prognostic. In this paper, artificial neural network are used to detect and classify the white blood cell (WBC) inside the acute leukemia blood samples. There are 25 features have been extracted from segmented WBC, which consist of shape, color and texture based features. Then, it have been fed up as the neural network inputs for the classification process in order to classify the segmented regions into two classes either B or T. The training algorithm for MLP network is Levenberg-Marquardt (LM). The MLP network achieves the highest testing accuracy of 96.99% for 4 hidden nodes at state of 5 by using the overall 25 input features. Thus, MLP network trained by using LM algorithm is suitable for acute leukemia cells detection in blood sample.</jats:p

Image enhancement technique for bone marrow

The 2nd International Malaysia-Ireland Joint Symposium on Engineering, Science and Business 2012 (IMiEJS2012) jointly organized by Universiti Malaysia Perlis and Athlone Institute of Technology in collaboration with The Ministry of Higher Education (MOHE) Malaysia, Education Malaysia and Malaysia Postgraduates Student Association Ireland (MyPSI), 18th - 19th June 2012 at Putra World Trade Center (PWTC), Kuala Lumpur, Malaysia.This paper presents a study on image enhancement technique for improving the features of blasts in acute leukemia bone marrow samples. The morphological features in bone marrow images could be improved if poor contrast could be eliminated. This study purposed two enhancement techniques, namely Local Contrast Stretching and Dark Contrast stretching. The results show that the proposed technique improves the image quality. Hence, these images can be viewed clearly by the hematologists for further analysis of the types of acute leukemia

Nucleus segmentation technique for acute leukemia

Link to publisher's homepage at http://ieeexplore.ieee.org/Leukemia is a disease that affects blood forming cells in the body. Early detection of the disease is necessary for proper treatment management. Abnormal white blood cells or blasts play important role for hematologists in their diagnostic process. Digital image processing technique could help them in their analysis and diagnosis by enhancing the visibility of the interested features of the WBC. In this paper, a global contrast stretching (GCS) and segmentation based on HSI (Hue, Saturation, Intensity) color space will be used to improve the image quality. Image enhancement is very important to increase the visual aspect of blast cells. The results show that the proposed image enhancement procedure is useful to extract the nucleus region in WBC images sample by using the same threshold value, for both ALL and AML images