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Licensed Unlicensed Requires Authentication Published by De Gruyter October 6, 2022

A novel deep learning approach for sickle cell anemia detection in human RBCs using an improved wrapper-based feature selection technique in microscopic blood smear images

  • Alagu S. ORCID logo EMAIL logo , Kavitha Ganesan and Bhoopathy Bagan K.


Sickle Cell Anemia (SCA) is a disorder in Red Blood Cells (RBCs) of human blood. Children under five years and pregnant women are mostly affected by SCA. Early diagnosis of this ailment can save lives. In recent years, the computer aided diagnosis of SCA is preferred to resolve this issue. A novel and effective deep learning approach for identification of sickle cell anemia is proposed in this work. Around nine hundred microscopic images of human red blood cells are obtained from the public database ‘erythrocytes IDB’. All the images are resized uniformly. About 2048 deep features are extracted from the fully connected layer of pre-trained model InceptionV3. These features are further subjected to classification using optimization-based methods. An improved wrapper-based feature selection technique is implemented using Multi- Objective Binary Grey Wolf Optimization (MO-BGWO) approach with KNN and SVM for classification. The detection of sickle cell is also performed using typical InceptionV3 model by using SoftMax layer. It is observed that the performance of the proposed system seems to be high when compared to the classification using the original InceptionV3 model. The results are validated by various evaluation metrics such as accuracy, precision, sensitivity, specificity and F1-score. The SVM classifier yields high accuracy of about 96%. The optimal subset of deep features along with SVM enhances the system performance in the proposed work. Thus, the proposed approach is appropriate for pathologists to take early clinical decisions on detection of sickle cells.

Corresponding author: Alagu S., Department of Electronics Engineering, Madras Institute of Technology, Chennai, India, E-mail:

  1. Research funding: Not applicable.

  2. Author contributions: (1) All authors contributed their ideas and concepts in identifying recent research problem. (2) Design of the work formed by corresponding author and it is corrected and approved by co-authors. (3) The acquisition of microscopic images from public database, analysis, interpretation of data for the work and drafting the work are carried out by corresponding author. (4) Further, critical review and revisions are guided by co-authors starting from spell check to intellectual content. All corrections are incorporated and approved by all. (5) The final manuscript is completely team work and sculptured by all authors. (6) The authors ensure the accuracy or integrity of the work. The proposed research article is unique which is not submitted before anywhere.

  3. Competing interests: The authors have no conflict of interest (financial, personal or professional) in connection with manuscript.

  4. Informed consent: Not applicable.

  5. Ethical approval: Not applicable.


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Received: 2021-04-29
Accepted: 2022-09-13
Published Online: 2022-10-06
Published in Print: 2023-04-25

© 2022 Walter de Gruyter GmbH, Berlin/Boston

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