A reliable auto-robust analysis of blood smear images for classification of microcytic hypochromic anemia using gray level matrices and gabor feature bank

Azam, B. and Rahman, S.U. and Irfan, M. and Awais, M. and Alshehri, O.M. and Saif, A. and Nahari, M.H. and Mahnashi, M.H. (2020) A reliable auto-robust analysis of blood smear images for classification of microcytic hypochromic anemia using gray level matrices and gabor feature bank. Entropy, 22 (9). ISSN 1099-4300

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Abstract

Accurate blood smear quantification with various blood cell samples is of great clinical importance. The conventional manual process of blood smear quantification is quite time consuming and is prone to errors. Therefore, this paper presents automatic detection of the most frequently occurring condition in human blood-microcytic hyperchromic anemia-which is the cause of various life-threatening diseases. This task has been done with segmentation of blood contents, i.e., Red Blood Cells (RBCs), White Blood Cells (WBCs), and platelets, in the first step. Then, the most influential features like geometric shape descriptors, Gray Level Co-occurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRLM), and Gabor features (mean squared energy and mean amplitude) are extracted from each of the RBCs. To discriminate the cells as hypochromic microcytes among other RBC classes, scanning is done at angles (0°, 45°, 90°, and 135°). To achieve high-level accuracy, Adaptive Synthetic (AdaSyn) sampling for imbalance learning is used to balance the datasets and locality sensitive discriminant analysis (LSDA) technique is used for feature reduction. Finally, upon using these features, classification of blood cells is done using the multilayer perceptual model and random forest learning algorithms. Performance in terms of accuracy was 96%, which is better than the performance of existing techniques. The final outcome of this work may be useful in the efforts to produce a cost-effective screening scheme that could make inexpensive screening for blood smear analysis available globally, thus providing early detection of these diseases. © 2020 by the authors.

Item Type:
Journal Article
Journal or Publication Title:
Entropy
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/3100/3109
Subjects:
ID Code:
148636
Deposited By:
Deposited On:
02 Nov 2020 16:45
Refereed?:
Yes
Published?:
Published
Last Modified:
02 Nov 2020 16:45