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Article

A Reliable Auto-Robust Analysis of Blood Smear Images for Classification of Microcytic Hypochromic Anemia Using Gray Level Matrices and Gabor Feature Bank

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Department of Computer Science and IT, University of Malakand, Chakdara 18801, Pakistan
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College of Engineering, Electrical Engineering Department, Najran University, Najran 61441, Saudi Arabia
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School of Computing and Communications, Lancaster University, Bailrigg, Lancaster LA1 4YW, UK
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Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Najran University, Najran 61441, Saudi Arabia
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Department of Medicinal Chemistry, Pharmacy School, Najran University, Najran 61441, Saudi Arabia
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Author to whom correspondence should be addressed.
Entropy 2020, 22(9), 1040; https://doi.org/10.3390/e22091040
Received: 15 July 2020 / Revised: 15 September 2020 / Accepted: 15 September 2020 / Published: 17 September 2020
(This article belongs to the Special Issue Reliability of Modern Electro-Mechanical Systems)
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. View Full-Text
Keywords: erythrocytes; RBCs; segmentation; classification; anemia; reliable erythrocytes; RBCs; segmentation; classification; anemia; reliable
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MDPI and ACS Style

Azam, B.; Ur Rahman, S.; Irfan, M.; Awais, M.; Alshehri, O.M.; Saif, A.; Nahari, M.H.; Mahnashi, M.H. A Reliable Auto-Robust Analysis of Blood Smear Images for Classification of Microcytic Hypochromic Anemia Using Gray Level Matrices and Gabor Feature Bank. Entropy 2020, 22, 1040. https://doi.org/10.3390/e22091040

AMA Style

Azam B, Ur Rahman S, Irfan M, Awais M, Alshehri OM, Saif A, Nahari MH, Mahnashi MH. A Reliable Auto-Robust Analysis of Blood Smear Images for Classification of Microcytic Hypochromic Anemia Using Gray Level Matrices and Gabor Feature Bank. Entropy. 2020; 22(9):1040. https://doi.org/10.3390/e22091040

Chicago/Turabian Style

Azam, Bakht, Sami Ur Rahman, Muhammad Irfan, Muhammad Awais, Osama M. Alshehri, Ahmed Saif, Mohammed H. Nahari, and Mater H. Mahnashi. 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, no. 9: 1040. https://doi.org/10.3390/e22091040

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