A Reliable Auto-Robust Analysis of Blood Smear Images for Classification of Microcytic Hypochromic Anemia Using Gray Level Matrices and Gabor Feature Bank
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 condi...
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MDPI AG
2020-09-01
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| Online Access: | https://www.mdpi.com/1099-4300/22/9/1040 |
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| author | Bakht Azam Sami Ur Rahman Muhammad Irfan Muhammad Awais Osama Mohammed Alshehri Ahmed Saif Mohammed Hassan Nahari Mater H. Mahnashi |
| author_facet | Bakht Azam Sami Ur Rahman Muhammad Irfan Muhammad Awais Osama Mohammed Alshehri Ahmed Saif Mohammed Hassan Nahari Mater H. Mahnashi |
| author_sort | Bakht Azam |
| collection | DOAJ |
| description | 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<inline-formula><math display="inline"><semantics><msup><mrow></mrow><mo>∘</mo></msup></semantics></math></inline-formula>, 45<inline-formula><math display="inline"><semantics><msup><mrow></mrow><mo>∘</mo></msup></semantics></math></inline-formula>, 90<inline-formula><math display="inline"><semantics><msup><mrow></mrow><mo>∘</mo></msup></semantics></math></inline-formula>, and 135<inline-formula><math display="inline"><semantics><msup><mrow></mrow><mo>∘</mo></msup></semantics></math></inline-formula>). 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. |
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| language | English |
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| spelling | doaj.art-68df64a1c09440698601f8bd5e8f3bf62023-11-20T14:06:45ZengMDPI AGEntropy1099-43002020-09-01229104010.3390/e22091040A Reliable Auto-Robust Analysis of Blood Smear Images for Classification of Microcytic Hypochromic Anemia Using Gray Level Matrices and Gabor Feature BankBakht Azam0Sami Ur Rahman1Muhammad Irfan2Muhammad Awais3Osama Mohammed Alshehri4Ahmed Saif5Mohammed Hassan Nahari6Mater H. Mahnashi7Department of Computer Science and IT, University of Malakand, Chakdara 18801, PakistanDepartment of Computer Science and IT, University of Malakand, Chakdara 18801, PakistanCollege of Engineering, Electrical Engineering Department, Najran University, Najran 61441, Saudi ArabiaSchool of Computing and Communications, Lancaster University, Bailrigg, Lancaster LA1 4YW, UKDepartment of Clinical Laboratory Sciences, College of Applied Medical Sciences, Najran University, Najran 61441, Saudi ArabiaDepartment of Clinical Laboratory Sciences, College of Applied Medical Sciences, Najran University, Najran 61441, Saudi ArabiaDepartment of Clinical Laboratory Sciences, College of Applied Medical Sciences, Najran University, Najran 61441, Saudi ArabiaDepartment of Medicinal Chemistry, Pharmacy School, Najran University, Najran 61441, Saudi ArabiaAccurate 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<inline-formula><math display="inline"><semantics><msup><mrow></mrow><mo>∘</mo></msup></semantics></math></inline-formula>, 45<inline-formula><math display="inline"><semantics><msup><mrow></mrow><mo>∘</mo></msup></semantics></math></inline-formula>, 90<inline-formula><math display="inline"><semantics><msup><mrow></mrow><mo>∘</mo></msup></semantics></math></inline-formula>, and 135<inline-formula><math display="inline"><semantics><msup><mrow></mrow><mo>∘</mo></msup></semantics></math></inline-formula>). 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.https://www.mdpi.com/1099-4300/22/9/1040erythrocytesRBCssegmentationclassificationanemiareliable |
| spellingShingle | Bakht Azam Sami Ur Rahman Muhammad Irfan Muhammad Awais Osama Mohammed Alshehri Ahmed Saif Mohammed Hassan Nahari Mater H. Mahnashi A Reliable Auto-Robust Analysis of Blood Smear Images for Classification of Microcytic Hypochromic Anemia Using Gray Level Matrices and Gabor Feature Bank Entropy erythrocytes RBCs segmentation classification anemia reliable |
| title | A Reliable Auto-Robust Analysis of Blood Smear Images for Classification of Microcytic Hypochromic Anemia Using Gray Level Matrices and Gabor Feature Bank |
| title_full | A Reliable Auto-Robust Analysis of Blood Smear Images for Classification of Microcytic Hypochromic Anemia Using Gray Level Matrices and Gabor Feature Bank |
| title_fullStr | A Reliable Auto-Robust Analysis of Blood Smear Images for Classification of Microcytic Hypochromic Anemia Using Gray Level Matrices and Gabor Feature Bank |
| title_full_unstemmed | A Reliable Auto-Robust Analysis of Blood Smear Images for Classification of Microcytic Hypochromic Anemia Using Gray Level Matrices and Gabor Feature Bank |
| title_short | A Reliable Auto-Robust Analysis of Blood Smear Images for Classification of Microcytic Hypochromic Anemia Using Gray Level Matrices and Gabor Feature Bank |
| title_sort | reliable auto robust analysis of blood smear images for classification of microcytic hypochromic anemia using gray level matrices and gabor feature bank |
| topic | erythrocytes RBCs segmentation classification anemia reliable |
| url | https://www.mdpi.com/1099-4300/22/9/1040 |
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