Texture Metric Driven Acute Lymphoid Leukemia Classification Using Artificial Neural Networks
M. Anto Bennet1, G. Diana2, U. Pooja3, N. Ramya4
1M. Anto Bennet, Professor, Department of Electronics and Communication Engineering, Vel Tech Chennai (Tamil Nadu), India.
2G. Diana, UG Student, Department of Electronics and Communication Engineering, Vel Tech Chennai (Tamil Nadu), India.
3U. Pooja, UG Student, Department of Electronics and Communication Engineering, Vel Tech Chennai (Tamil Nadu), India.
4N. Ramya, UG Student, Department of Electronics and Communication Engineering, Vel Tech Chennai (Tamil Nadu), India.
Manuscript received on 23 April 2019 | Revised Manuscript received on 02 May 2019 | Manuscript Published on 07 May 2019 | PP: 152-156 | Volume-7 Issue-6S3 April 2019 | Retrieval Number: F1030376S19/2019©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: One of the human body’s most important part is blood. Blood has terribly important functions for the body among others. In recent years, the incidence of many blood diseases such as leukemia, anemia and some malignant tumors are increasing. Leukemia is a disease, which suppress the production of normal blood cells. Here, we propose a method to increase the accuracy of classification of WBC cells using Artificial Neural Network classifier. DWT based transformations are used for removing the redundant information. Shape, statistical and GLCM based features are extracted from the segmented regions of Blood Microscopic Images. RGB to L*a*b conversion is used as pre-processing step so that classification of cells based on color can be more concentrated. Rotational and scale invariant texture based texture classification is used for texture segmentation to get the region of interest. Thus, we employ the concept of Artificial Neural Network for analysing the Blood related diseases.
Keywords: ANN (Artificial Neural Network) Classification, WBC’s (White Blood Cells), CIELAB Color Space.
Scope of the Article: Classification