Treffer: Morphological Abnormalities Classification of Red Blood Cells Using Fusion Method on Imbalance Datasets.
Title:
Morphological Abnormalities Classification of Red Blood Cells Using Fusion Method on Imbalance Datasets.
Authors:
Dhar P; Medical Imaging Laboratory, Department of Computer Science and Engineering, National Institute of Technology Silchar, Silchar, Assam, India., Suganya Devi K; Medical Imaging Laboratory, Department of Computer Science and Engineering, National Institute of Technology Silchar, Silchar, Assam, India., Bhattacharjee R; Institute of Technical Education and Research, Department of Computer Science and Engineering, Siksha O Anusandhan University, Bhubaneswar, Odisha, India., Srinivasan P; Department of Physics, National Institute of Technology Silchar, Silchar, Assam, India.
Source:
Microscopy research and technique [Microsc Res Tech] 2025 May; Vol. 88 (5), pp. 1566-1581. Date of Electronic Publication: 2025 Jan 27.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Wiley-Liss Country of Publication: United States NLM ID: 9203012 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1097-0029 (Electronic) Linking ISSN: 1059910X NLM ISO Abbreviation: Microsc Res Tech Subsets: MEDLINE
Imprint Name(s):
Original Publication: New York, NY : Wiley-Liss, c1992-
MeSH Terms:
References:
Abdou, M. A. H., T. T. Truong, A. Dykyy, P. Ferreira, and E. Jul. 2022. “Capillarynet: An Automated System to Quantify Skin Capillary Density and Red Blood Cell Velocity From Handheld Vital Microscopy.” Artificial Intelligence in Medicine 127: 102287.
Alomari, Y. M., S. N. H. S. Abdullah, R. Z. Azma, and K. Omar. 2014. “Automatic Detection and Quantification of Wbcs and Rbcs Using Iterative Structured Circle Detection Algorithm.” Computational and Mathematical Methods in Medicine 1.
Anilkumar, K., V. Manoj, and T. Sagi. 2020. “A Survey on Image Segmentation of Blood and Bone Marrow Smear Images With Emphasis to Automated Detection of Leukemia.” Biocybernetics and Biomedical Engineering 40, no. 4: 1406–1420.
Bai, X., C. Sun, and F. Zhou. 2009. “Splitting Touching Cells Based on Concave Points and Ellipse Fitting.” Pattern Recognition 42, no. 11: 2434–2446.
Cao, F., M. Cai, J. Chu, J. Zhao, and Z. Zhou. 2017. “A Novel Segmentation Algorithm for Nucleus in White Blood Cells Based on Low‐Rank Representation.” Neural Computing and Applications 28: 503–511.
Das, P. K., S. Meher, R. Panda, and A. Abraham. 2021. “An Efficient Blood‐Cell Segmentation for the Detection of Hematological Disorders.” IEEE Transactions on Cybernetics 52, no. 10: 10615–10626.
Dhar, P., K. S. Devi, K. Sekar, and P. Srinivasan. 2023. “Morphology of Red Blood Cells Classification Using Deep Learning Approach.” 2023 IEEE 3rd International Conference on Technology, Engineering, Management for Societal Impact Using Marketing, Entrepreneurship and Talent (TEMSMET), IEEE: 1–6.
Dhar, P., K. Suganya Devi, and P. Srinivasan. 2023. “CNN‐RSVM: A Hybrid Approach for Classification of Poikilocytosis Using Convolutional Neural Network and Radial Kernel Basis Support Vector Machine.” Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 11: 1–12.
Dhar, P., K. S. Devi, S. K. Satti, and P. Srinivasan. 2023. “An Hybrid Soft Attention Based Xgboost Model for Classification of Poikilocytosis Blood Cells.” Evolving Systems 15. https://doi.org/10.1007/s12530‐023‐09549‐2.
Dhar, P., S. D. Kothandapani, S. K. Satti, and S. Padmanabhan. 2023a. “HPKNN: Hyper‐Parameter Optimized Knn Classifier for Classification of Poikilocytosis.” International Journal of Imaging Systems and Technology 33, no. 3: 928–950. https://doi.org/10.1002/ima.22841.
Dhar, P., D. K. Suganya, S. K. Satti, and P. Srinivasan. 2022b. “Efficient Detection and Partitioning of Overlapped Red Blood Cells Using Image Processing Approach.” Innovations in Systems and Software Engineering 10: 1–13. https://doi.org/10.1007/s11334‐022‐00478‐y.
Dimauro, G., M. E. Griseta, M. G. Camporeale, F. Clemente, A. Guarini, and R. Maglietta. 2023. “An Intelligent Non‐Invasive System for Automated Diagnosis of Anemia Exploiting a Novel Dataset.” Artificial Intelligence in Medicine 136: 102477.
Elen, A., and M. K. Turan. 2018. “A New Approach for Fully Automated Segmentation of Peripheral Blood Smears.” International Journal of Advances in Applied Sciences 5, no. 1: 81–93.
Gharipour, A., and A. W. C. Liew. 2016. “Segmentation of Cell Nuclei in Fluorescence Microscopy Images: An Integrated Framework Using Level Set Segmentation and Touching‐Cell Splitting.” Pattern Recognition 58: 1–11.
Govind, D., B. R. Lutnick, J. E. Tomaszewski, and P. Sarder. 2018. “Automated Erythrocyte Detection and Classification From Whole Slide Images.” Journal of Medical Imaging 5, no. 2: 027501.
Han, X., Y. Fu, and H. Zhang. 2012. “A Fast Two‐Step Marker‐Controlled Watershed Image Segmentation Method.” 2012 IEEE International Conference on Mechatronics and Automation, IEEE 3: 1375–1380.
Hari, J., A. S. Prasad, and S. K. Rao. 2014. “Separation and Counting of Blood Cells Using Geometrical Features and Distance Transformed Watershed.” 2014 2nd International Conference on Devices, Circuits and Systems (ICDCS), IEEE 1: 1–5.
Hu, K., K. Chen, X. He, et al. 2020. “Automatic Segmentation of Intracerebral Hemorrhage in Ct Images Using Encoder–Decoder Convolutional Neural Network.” Information Processing & Management 57, no. 6: 102352.
Hu, R., J. Gan, X. Zhu, T. Liu, and X. Shi. 2022. “Multi‐Task Multi‐Modality Svm for Early Covid‐19 Diagnosis Using Chest Ct Data.” Information Processing & Management 59, no. 1: 102782.
Jiang, Z., X. Liu, Z. Yan, W. Gu, and J. Jiang. 2021. “Improved Detection Performance in Blood Cell Count by an Attention‐Guided Deep Learning Method.” OSA Continuum 4, no. 2: 323–333.
Jones, K. W. 2009. “Evaluation of Cell Morphology and Introduction to Platelet and White Blood Cell Morphology.” Clinical Hematology and Fundamentals of Hemostasis 5: 93–112.
Korranat, N., T. H. Chalidabhongse, D. Palasuwan, N. Anantrasirichai, and A. Palasuwan. 2020. “Red Blood Cell Segmentation With Overlapping Cell Separation and Classification on Imbalanced Dataset.” arXiv Preprint arXiv.
LaTorre, A., L. Alonso‐Nanclares, S. Muelas, J. Peña, and J. DeFelipe. 2013. “Segmentation of Neuronal Nuclei Based on Clump Splitting and a Two‐Step Binarization of Images.” Expert Systems with Applications 40, no. 16: 6521–6530.
Lee, H., and Y. P. P. Chen. 2014. “Cell Morphology Based Classification for Red Cells in Blood Smear Images.” Pattern Recognition Letters 49: 155–161.
Miró‐Nicolau, M., B. Moyà‐Alcover, M. González‐Hidalgo, and A. Jaume‐i Capó. 2020. “Segmenting Overlapped Objects in Images. A Study to Support the Diagnosis of Sickle Cell Disease.” arXiv Preprint arXiv.
Mohan, H. 2018. Textbook of Pathology. New Delhi: Jaypee Brothers Medical Publishers.
Panagiotakis, C., and A. A. Argyros. 2018. “Cell Segmentation via Region‐Based Ellipse Fitting.” 2018 25th IEEE International Conference on Image Processing (ICIP), IEEE 6: 2426–2430.
Pasupa, K., S. Tungjitnob, and S. Vatathanavaro. 2020. “Semi‐Supervised Learning With Deep Convolutional Generative Adversarial Networks for Canine Red Blood Cells Morphology Classification.” Multimedia Tools and Applications 79, no. 45: 34209–34226.
Prasad, D. K., M. K. Leung, and C. Quek. 2013. “Ellifit: An Unconstrained, Non‐Iterative, Least Squares Based Geometric Ellipse Fitting Method.” Pattern Recognition 46, no. 5: 1449–1465.
Putzu, L., G. Caocci, and C. Di Ruberto. 2014. “Leucocyte Classification for Leukaemia Detection Using Image Processing Techniques.” Artificial Intelligence in Medicine 62, no. 3: 179–191.
Romero‐Rondón, M. F., L. Melissa Sanabria‐Rosas, L. X. Bautista‐Rozo, and A. Mendoza‐Castellanos. 2016. “Algorithm for Detection of Overlapped Red Blood Cells in Microscopic Images of Blood Smears.” Dyna 83, no. 198: 187–194.
Safuan, S. N. M., M. R. M. Tomari, and W. N. W. Zakaria. 2018. “White Blood Cell (Wbc) Counting Analysis in Blood Smear Images Using Various Color Segmentation Methods.” Measurement 116: 543–555.
Shahzad, M., A. I. Umar, S. H. Shirazi, et al. 2022. “Identification of Anemia and Its Severity Level in a Peripheral Blood Smear Using 3‐Tier Deep Neural Network.” Applied Sciences 12, no. 10: 5030.
Shakarami, A., M. B. Menhaj, A. Mahdavi‐Hormat, and H. Tarrah. 2021. “A Fast and Yet Efficient Yolov3 for Blood Cell Detection.” Biomedical Signal Processing and Control 66: 102495.
Sharif, J. M., M. Miswan, M. Ngadi, M. S. H. Salam, and M. M. Bin Abdul Jamil. 2012. “Red Blood Cell Segmentation Using Masking and Watershed Algorithm: A Preliminary Study.” 2012 International Conference on Biomedical Engineering (ICoBE), IEEE 1: 258–262.
Sheeba, F., M. T. T. Hannah, and J. J. Mammen. 2010. “Segmentation and Reversible Watermarking of Peripheral Blood Smear Images.” 2010 IEEE Fifth International Conference on Bio‐Inspired Computing: Theories and Applications (BIC‐TA), IEEE 8466: 1373–1376.
Shirazi, S. H., A. I. Umar, N. Haq, S. Naz, M. I. Razzak, and A. Zaib. 2018. “Extreme Learning Machine Based Microscopic Red Blood Cells Classification.” Cluster Computing 21, no. 1: 691–701.
Suganya Devi, K., G. Arutperumjothi, and P. Srinivasan. 2021. “Diagnosis Evaluation and Interpretation of Qualitative Abnormalities in Peripheral Blood Smear Images—A Review.” In Health Informatics: A Computational Perspective in Healthcare, edited by R. Patgiri, A. Biswas, and P. Roy, 341–365. Singapore: Springer. https://doi.org/10.1007/978‐981‐15‐9735‐0_17.
Tessema, A. W., M. A. Mohammed, G. L. Simegn, and T. C. Kwa. 2021. “Quantitative Analysis of Blood Cells From Microscopic Images Using Convolutional Neural Network.” Medical & Biological Engineering & Computing 59, no. 1: 143–152.
Tulsani, H., S. Saxena, and N. Yadav. 2013. “Segmentation Using Morphological Watershed Transformation for Counting Blood Cells.” International Journal of Computer Applications in Information Technology 2, no. 3: 28–36.
WHO. 2015. “The Global Prevalence of Anaemia in 2011.”.
Wong, A., N. Anantrasirichai, T. H. Chalidabhongse, D. Palasuwan, A. Palasuwan, and D. Bull. 2021. “Analysis of Vision‐Based Abnormal Red Blood Cell Classification.” arXiv Preprint arXiv.
Yi, F., I. Moon, and B. Javidi. 2016. “Cell Morphology‐Based Classification of Red Blood Cells Using Holographic Imaging Informatics.” Biomedical Optics Express 7, no. 6: 2385–2399.
Yuan, K., Z. Tian, J. Zou, Y. Bai, and Q. You. 2011. “Brain Ct Image Database Building for Computer‐Aided Diagnosis Using Content‐Based Image Retrieval.” Information Processing & Management 47, no. 2: 176–185.
Zafari, S., T. Eerola, J. Sampo, H. Kälviäinen, and H. Haario. 2015. “Segmentation of Overlapping Elliptical Objects in Silhouette Images.” IEEE Transactions on Image Processing 24, no. 12: 5942–5952.
Zeng, L., H. Li, T. Xiao, F. Shen, and Z. Zhong. 2022. “Graph Convolutional Network With Sample and Feature Weights for Alzheimer's Disease Diagnosis.” Information Processing & Management 59, no. 4: 102952.
Zhan, M., X. Shi, F. Liu, and R. Hu. 2023. “IGCNN‐FC: Boosting Interpretability and Generalization of Convolutional Neural Networks for Few Chest x‐Rays Analysis.” Information Processing & Management 60, no. 3: 103258.
Zhang, Y. D., S. C. Satapathy, D. S. Guttery, J. M. Górriz, and S. H. Wang. 2021. “Improved Breast Cancer Classification Through Combining Graph Convolutional Network and Convolutional Neural Network.” Information Processing & Management 58, no. 2: 102439.
Alomari, Y. M., S. N. H. S. Abdullah, R. Z. Azma, and K. Omar. 2014. “Automatic Detection and Quantification of Wbcs and Rbcs Using Iterative Structured Circle Detection Algorithm.” Computational and Mathematical Methods in Medicine 1.
Anilkumar, K., V. Manoj, and T. Sagi. 2020. “A Survey on Image Segmentation of Blood and Bone Marrow Smear Images With Emphasis to Automated Detection of Leukemia.” Biocybernetics and Biomedical Engineering 40, no. 4: 1406–1420.
Bai, X., C. Sun, and F. Zhou. 2009. “Splitting Touching Cells Based on Concave Points and Ellipse Fitting.” Pattern Recognition 42, no. 11: 2434–2446.
Cao, F., M. Cai, J. Chu, J. Zhao, and Z. Zhou. 2017. “A Novel Segmentation Algorithm for Nucleus in White Blood Cells Based on Low‐Rank Representation.” Neural Computing and Applications 28: 503–511.
Das, P. K., S. Meher, R. Panda, and A. Abraham. 2021. “An Efficient Blood‐Cell Segmentation for the Detection of Hematological Disorders.” IEEE Transactions on Cybernetics 52, no. 10: 10615–10626.
Dhar, P., K. S. Devi, K. Sekar, and P. Srinivasan. 2023. “Morphology of Red Blood Cells Classification Using Deep Learning Approach.” 2023 IEEE 3rd International Conference on Technology, Engineering, Management for Societal Impact Using Marketing, Entrepreneurship and Talent (TEMSMET), IEEE: 1–6.
Dhar, P., K. Suganya Devi, and P. Srinivasan. 2023. “CNN‐RSVM: A Hybrid Approach for Classification of Poikilocytosis Using Convolutional Neural Network and Radial Kernel Basis Support Vector Machine.” Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 11: 1–12.
Dhar, P., K. S. Devi, S. K. Satti, and P. Srinivasan. 2023. “An Hybrid Soft Attention Based Xgboost Model for Classification of Poikilocytosis Blood Cells.” Evolving Systems 15. https://doi.org/10.1007/s12530‐023‐09549‐2.
Dhar, P., S. D. Kothandapani, S. K. Satti, and S. Padmanabhan. 2023a. “HPKNN: Hyper‐Parameter Optimized Knn Classifier for Classification of Poikilocytosis.” International Journal of Imaging Systems and Technology 33, no. 3: 928–950. https://doi.org/10.1002/ima.22841.
Dhar, P., D. K. Suganya, S. K. Satti, and P. Srinivasan. 2022b. “Efficient Detection and Partitioning of Overlapped Red Blood Cells Using Image Processing Approach.” Innovations in Systems and Software Engineering 10: 1–13. https://doi.org/10.1007/s11334‐022‐00478‐y.
Dimauro, G., M. E. Griseta, M. G. Camporeale, F. Clemente, A. Guarini, and R. Maglietta. 2023. “An Intelligent Non‐Invasive System for Automated Diagnosis of Anemia Exploiting a Novel Dataset.” Artificial Intelligence in Medicine 136: 102477.
Elen, A., and M. K. Turan. 2018. “A New Approach for Fully Automated Segmentation of Peripheral Blood Smears.” International Journal of Advances in Applied Sciences 5, no. 1: 81–93.
Gharipour, A., and A. W. C. Liew. 2016. “Segmentation of Cell Nuclei in Fluorescence Microscopy Images: An Integrated Framework Using Level Set Segmentation and Touching‐Cell Splitting.” Pattern Recognition 58: 1–11.
Govind, D., B. R. Lutnick, J. E. Tomaszewski, and P. Sarder. 2018. “Automated Erythrocyte Detection and Classification From Whole Slide Images.” Journal of Medical Imaging 5, no. 2: 027501.
Han, X., Y. Fu, and H. Zhang. 2012. “A Fast Two‐Step Marker‐Controlled Watershed Image Segmentation Method.” 2012 IEEE International Conference on Mechatronics and Automation, IEEE 3: 1375–1380.
Hari, J., A. S. Prasad, and S. K. Rao. 2014. “Separation and Counting of Blood Cells Using Geometrical Features and Distance Transformed Watershed.” 2014 2nd International Conference on Devices, Circuits and Systems (ICDCS), IEEE 1: 1–5.
Hu, K., K. Chen, X. He, et al. 2020. “Automatic Segmentation of Intracerebral Hemorrhage in Ct Images Using Encoder–Decoder Convolutional Neural Network.” Information Processing & Management 57, no. 6: 102352.
Hu, R., J. Gan, X. Zhu, T. Liu, and X. Shi. 2022. “Multi‐Task Multi‐Modality Svm for Early Covid‐19 Diagnosis Using Chest Ct Data.” Information Processing & Management 59, no. 1: 102782.
Jiang, Z., X. Liu, Z. Yan, W. Gu, and J. Jiang. 2021. “Improved Detection Performance in Blood Cell Count by an Attention‐Guided Deep Learning Method.” OSA Continuum 4, no. 2: 323–333.
Jones, K. W. 2009. “Evaluation of Cell Morphology and Introduction to Platelet and White Blood Cell Morphology.” Clinical Hematology and Fundamentals of Hemostasis 5: 93–112.
Korranat, N., T. H. Chalidabhongse, D. Palasuwan, N. Anantrasirichai, and A. Palasuwan. 2020. “Red Blood Cell Segmentation With Overlapping Cell Separation and Classification on Imbalanced Dataset.” arXiv Preprint arXiv.
LaTorre, A., L. Alonso‐Nanclares, S. Muelas, J. Peña, and J. DeFelipe. 2013. “Segmentation of Neuronal Nuclei Based on Clump Splitting and a Two‐Step Binarization of Images.” Expert Systems with Applications 40, no. 16: 6521–6530.
Lee, H., and Y. P. P. Chen. 2014. “Cell Morphology Based Classification for Red Cells in Blood Smear Images.” Pattern Recognition Letters 49: 155–161.
Miró‐Nicolau, M., B. Moyà‐Alcover, M. González‐Hidalgo, and A. Jaume‐i Capó. 2020. “Segmenting Overlapped Objects in Images. A Study to Support the Diagnosis of Sickle Cell Disease.” arXiv Preprint arXiv.
Mohan, H. 2018. Textbook of Pathology. New Delhi: Jaypee Brothers Medical Publishers.
Panagiotakis, C., and A. A. Argyros. 2018. “Cell Segmentation via Region‐Based Ellipse Fitting.” 2018 25th IEEE International Conference on Image Processing (ICIP), IEEE 6: 2426–2430.
Pasupa, K., S. Tungjitnob, and S. Vatathanavaro. 2020. “Semi‐Supervised Learning With Deep Convolutional Generative Adversarial Networks for Canine Red Blood Cells Morphology Classification.” Multimedia Tools and Applications 79, no. 45: 34209–34226.
Prasad, D. K., M. K. Leung, and C. Quek. 2013. “Ellifit: An Unconstrained, Non‐Iterative, Least Squares Based Geometric Ellipse Fitting Method.” Pattern Recognition 46, no. 5: 1449–1465.
Putzu, L., G. Caocci, and C. Di Ruberto. 2014. “Leucocyte Classification for Leukaemia Detection Using Image Processing Techniques.” Artificial Intelligence in Medicine 62, no. 3: 179–191.
Romero‐Rondón, M. F., L. Melissa Sanabria‐Rosas, L. X. Bautista‐Rozo, and A. Mendoza‐Castellanos. 2016. “Algorithm for Detection of Overlapped Red Blood Cells in Microscopic Images of Blood Smears.” Dyna 83, no. 198: 187–194.
Safuan, S. N. M., M. R. M. Tomari, and W. N. W. Zakaria. 2018. “White Blood Cell (Wbc) Counting Analysis in Blood Smear Images Using Various Color Segmentation Methods.” Measurement 116: 543–555.
Shahzad, M., A. I. Umar, S. H. Shirazi, et al. 2022. “Identification of Anemia and Its Severity Level in a Peripheral Blood Smear Using 3‐Tier Deep Neural Network.” Applied Sciences 12, no. 10: 5030.
Shakarami, A., M. B. Menhaj, A. Mahdavi‐Hormat, and H. Tarrah. 2021. “A Fast and Yet Efficient Yolov3 for Blood Cell Detection.” Biomedical Signal Processing and Control 66: 102495.
Sharif, J. M., M. Miswan, M. Ngadi, M. S. H. Salam, and M. M. Bin Abdul Jamil. 2012. “Red Blood Cell Segmentation Using Masking and Watershed Algorithm: A Preliminary Study.” 2012 International Conference on Biomedical Engineering (ICoBE), IEEE 1: 258–262.
Sheeba, F., M. T. T. Hannah, and J. J. Mammen. 2010. “Segmentation and Reversible Watermarking of Peripheral Blood Smear Images.” 2010 IEEE Fifth International Conference on Bio‐Inspired Computing: Theories and Applications (BIC‐TA), IEEE 8466: 1373–1376.
Shirazi, S. H., A. I. Umar, N. Haq, S. Naz, M. I. Razzak, and A. Zaib. 2018. “Extreme Learning Machine Based Microscopic Red Blood Cells Classification.” Cluster Computing 21, no. 1: 691–701.
Suganya Devi, K., G. Arutperumjothi, and P. Srinivasan. 2021. “Diagnosis Evaluation and Interpretation of Qualitative Abnormalities in Peripheral Blood Smear Images—A Review.” In Health Informatics: A Computational Perspective in Healthcare, edited by R. Patgiri, A. Biswas, and P. Roy, 341–365. Singapore: Springer. https://doi.org/10.1007/978‐981‐15‐9735‐0_17.
Tessema, A. W., M. A. Mohammed, G. L. Simegn, and T. C. Kwa. 2021. “Quantitative Analysis of Blood Cells From Microscopic Images Using Convolutional Neural Network.” Medical & Biological Engineering & Computing 59, no. 1: 143–152.
Tulsani, H., S. Saxena, and N. Yadav. 2013. “Segmentation Using Morphological Watershed Transformation for Counting Blood Cells.” International Journal of Computer Applications in Information Technology 2, no. 3: 28–36.
WHO. 2015. “The Global Prevalence of Anaemia in 2011.”.
Wong, A., N. Anantrasirichai, T. H. Chalidabhongse, D. Palasuwan, A. Palasuwan, and D. Bull. 2021. “Analysis of Vision‐Based Abnormal Red Blood Cell Classification.” arXiv Preprint arXiv.
Yi, F., I. Moon, and B. Javidi. 2016. “Cell Morphology‐Based Classification of Red Blood Cells Using Holographic Imaging Informatics.” Biomedical Optics Express 7, no. 6: 2385–2399.
Yuan, K., Z. Tian, J. Zou, Y. Bai, and Q. You. 2011. “Brain Ct Image Database Building for Computer‐Aided Diagnosis Using Content‐Based Image Retrieval.” Information Processing & Management 47, no. 2: 176–185.
Zafari, S., T. Eerola, J. Sampo, H. Kälviäinen, and H. Haario. 2015. “Segmentation of Overlapping Elliptical Objects in Silhouette Images.” IEEE Transactions on Image Processing 24, no. 12: 5942–5952.
Zeng, L., H. Li, T. Xiao, F. Shen, and Z. Zhong. 2022. “Graph Convolutional Network With Sample and Feature Weights for Alzheimer's Disease Diagnosis.” Information Processing & Management 59, no. 4: 102952.
Zhan, M., X. Shi, F. Liu, and R. Hu. 2023. “IGCNN‐FC: Boosting Interpretability and Generalization of Convolutional Neural Networks for Few Chest x‐Rays Analysis.” Information Processing & Management 60, no. 3: 103258.
Zhang, Y. D., S. C. Satapathy, D. S. Guttery, J. M. Górriz, and S. H. Wang. 2021. “Improved Breast Cancer Classification Through Combining Graph Convolutional Network and Convolutional Neural Network.” Information Processing & Management 58, no. 2: 102439.
Contributed Indexing:
Keywords: abnormal red blood cells; anisocytosis; class imbalance; features fusion; poikilocytosis
Entry Date(s):
Date Created: 20250128 Date Completed: 20250503 Latest Revision: 20250503
Update Code:
20250505
DOI:
10.1002/jemt.24786
PMID:
39871114
Database:
MEDLINE
Weitere Informationen
Red blood cells (RBCs) or Erythrocytes are essential components of the human body and they transport oxygen INLINEMATH from the lungs to the body's tissues, regulate INLINEMATH balance, and support the immune system. Abnormalities in RBC shapes (Poikilocytosis) and sizes (Anisocytosis) can impede oxygen-carrying capacity, leading to conditions such as anemia, thalassemia, McLeod Syndrome, liver disease, and so on. Hematologists typically spend considerable time manually examining RBC's shapes and sizes using a microscope and it is time-consuming. The proposed LSTM based neural network (NN) deep-learning strategy helps to classify abnormal RBCs automatically and accurately and overcome blood-related disorders at an early stage. After data processing, traditional and high-level features are fused to clearly distinguish between abnormal RBC classes. Class imbalance favors the dominant class, resulting in biased forecasts. To address class imbalance, a custom loss function is generated by integrating class weights and loss functions before feeding fused features to the NN classifier. Specifically, the loss function is designed to assign higher penalties to the misclassification of underrepresented classes, ensuring that the model is more sensitive to these classes during training. This is achieved by integrating class weights directly into the cross-entropy loss calculation, thereby balancing the influence of each class on the model's learning process. The proposed approach's performance is evaluated using the publicly accessible Chula-PIC-Lab dataset and privately gathered dataset from the Cachar Cancer Hospital and Research Centre (CCHRC) in Assam, India. The proposed approach achieves an average of INLINEMATH and INLINEMATHINLINEMATH -score and accuracy on the Chula-PIC-Lab dataset and an average of INLINEMATH and INLINEMATHINLINEMATH -score and accuracy on the CCHRC dataset for INLINEMATH and INLINEMATH classes and surpasses benchmark models including Custom CNN, Custom LSTM, Efficient Net-B1, SMOTE, Hybrid NN, and HPKNN.
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