A deep learning AlexNet model for classification of red blood cells in sickle cell anemia
Sickle cell anemia (SCA) is a serious hematological disorder, where affected patients are frequently hospitalized throughout a lifetime and even can cause death. The manual method of detecting and classifying abnormal cells of SCA patient blood film through a microscope is time-consuming, tedious, p...
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| Format: | Article |
| Language: | English |
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Institute of Advanced Engineering and Science
2020
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| Online Access: | http://eprints.utm.my/92681/1/RubitaSudirman2020_ADeepLearningAlexNetModelforClassification.pdf |
| _version_ | 1796865472213286912 |
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| author | Abdulkarim, Hajara Aliyu Abdul Razak, Mohd. Azhar Sudirman, Rubita Ramli, Norhafizah |
| author_facet | Abdulkarim, Hajara Aliyu Abdul Razak, Mohd. Azhar Sudirman, Rubita Ramli, Norhafizah |
| author_sort | Abdulkarim, Hajara Aliyu |
| collection | ePrints |
| description | Sickle cell anemia (SCA) is a serious hematological disorder, where affected patients are frequently hospitalized throughout a lifetime and even can cause death. The manual method of detecting and classifying abnormal cells of SCA patient blood film through a microscope is time-consuming, tedious, prone to error, and require a trained hematologist. The affected patient has many cell shapes that show important biomechanical characteristics. Hence, having an effective way of classifying the abnormalities present in the SCA disease will give a better insight into managing the concerned patient's life. This work proposed algorithm in two-phase firstly, automation of red blood cells (RBCs) extraction to identify the RBC region of interest (ROI) from the patient’s blood smear image. Secondly, deep learning AlexNet model is employed to classify and predict the abnormalities presence in SCA patients. The study was performed with (over 9,000 single RBC images) taken from 130 SCA patient each class having 750 cells. To develop a shape factor quantification and general multiscale shape analysis. We reveal that the proposed framework can classify 15 types of RBC shapes including normal in an automated manner with a deep AlexNet transfer learning model. The cell's name classification prediction accuracy, sensitivity, specificity, and precision of 95.92%, 77%, 98.82%, and 90% were achieved, respectively. |
| first_indexed | 2024-03-05T20:57:32Z |
| format | Article |
| id | utm.eprints-92681 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-03-05T20:57:32Z |
| publishDate | 2020 |
| publisher | Institute of Advanced Engineering and Science |
| record_format | dspace |
| spelling | utm.eprints-926812021-10-28T10:25:20Z http://eprints.utm.my/92681/ A deep learning AlexNet model for classification of red blood cells in sickle cell anemia Abdulkarim, Hajara Aliyu Abdul Razak, Mohd. Azhar Sudirman, Rubita Ramli, Norhafizah TK Electrical engineering. Electronics Nuclear engineering Sickle cell anemia (SCA) is a serious hematological disorder, where affected patients are frequently hospitalized throughout a lifetime and even can cause death. The manual method of detecting and classifying abnormal cells of SCA patient blood film through a microscope is time-consuming, tedious, prone to error, and require a trained hematologist. The affected patient has many cell shapes that show important biomechanical characteristics. Hence, having an effective way of classifying the abnormalities present in the SCA disease will give a better insight into managing the concerned patient's life. This work proposed algorithm in two-phase firstly, automation of red blood cells (RBCs) extraction to identify the RBC region of interest (ROI) from the patient’s blood smear image. Secondly, deep learning AlexNet model is employed to classify and predict the abnormalities presence in SCA patients. The study was performed with (over 9,000 single RBC images) taken from 130 SCA patient each class having 750 cells. To develop a shape factor quantification and general multiscale shape analysis. We reveal that the proposed framework can classify 15 types of RBC shapes including normal in an automated manner with a deep AlexNet transfer learning model. The cell's name classification prediction accuracy, sensitivity, specificity, and precision of 95.92%, 77%, 98.82%, and 90% were achieved, respectively. Institute of Advanced Engineering and Science 2020-06 Article PeerReviewed application/pdf en http://eprints.utm.my/92681/1/RubitaSudirman2020_ADeepLearningAlexNetModelforClassification.pdf Abdulkarim, Hajara Aliyu and Abdul Razak, Mohd. Azhar and Sudirman, Rubita and Ramli, Norhafizah (2020) A deep learning AlexNet model for classification of red blood cells in sickle cell anemia. IAES International Journal of Artificial Intelligence, 9 (2). pp. 221-228. ISSN 2089-4872 http://dx.doi.org/10.11591/ijai.v9.i2.pp221-228 DOI:10.11591/ijai.v9.i2.pp221-228 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Abdulkarim, Hajara Aliyu Abdul Razak, Mohd. Azhar Sudirman, Rubita Ramli, Norhafizah A deep learning AlexNet model for classification of red blood cells in sickle cell anemia |
| title | A deep learning AlexNet model for classification of red blood cells in sickle cell anemia |
| title_full | A deep learning AlexNet model for classification of red blood cells in sickle cell anemia |
| title_fullStr | A deep learning AlexNet model for classification of red blood cells in sickle cell anemia |
| title_full_unstemmed | A deep learning AlexNet model for classification of red blood cells in sickle cell anemia |
| title_short | A deep learning AlexNet model for classification of red blood cells in sickle cell anemia |
| title_sort | deep learning alexnet model for classification of red blood cells in sickle cell anemia |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://eprints.utm.my/92681/1/RubitaSudirman2020_ADeepLearningAlexNetModelforClassification.pdf |
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