ConcatNeXt: An automated blood cell classification with a new deep convolutional neural network
Article
Erten, Mehmet, Barua, Prabal Datta, Dogan, Sengul, Tuncer, Turker, Tan, Ru‑San and Acharya, U. R.. 2024. "ConcatNeXt: An automated blood cell classification with a new deep convolutional neural network." Multimedia Tools and Applications. https://doi.org/10.1007/s11042-024-19899-x
| Article Title | ConcatNeXt: An automated blood cell classification with a new deep convolutional neural network |
|---|---|
| ERA Journal ID | 18083 |
| Article Category | Article |
| Authors | Erten, Mehmet, Barua, Prabal Datta, Dogan, Sengul, Tuncer, Turker, Tan, Ru‑San and Acharya, U. R. |
| Journal Title | Multimedia Tools and Applications |
| Number of Pages | 19 |
| Year | 2024 |
| Publisher | Springer |
| Place of Publication | United States |
| ISSN | 1380-7501 |
| 1573-7721 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1007/s11042-024-19899-x |
| Web Address (URL) | https://link.springer.com/article/10.1007/s11042-024-19899-x |
| Abstract | Examining peripheral blood smears is valuable in clinical settings, yet manual identification of blood cells proves time-consuming. To address this, an automated blood cell image classification system is crucial. Our objective is to develop a precise automated model for detecting various blood cell types, leveraging a novel deep learning architecture. We harnessed a publicly available dataset of 17,092 blood cell images categorized into eight classes. Our innovation lies in ConcatNeXt, a new convolutional neural network. In the spirit of Geoffrey Hinton's approach, we adapted ConvNeXt by substituting the Gaussian error linear unit with a rectified linear unit and layer normalization with batch normalization. We introduced depth concatenation blocks to fuse information effectively and incorporated a patchify layer. Integrating ConcatNeXt with nested patch-based deep feature engineering, featuring downstream iterative neighborhood component analysis and support vector machine-based functions, establishes a comprehensive approach. ConcatNeXt achieved notable validation and test accuracies of 97.43% and 97.77%, respectively. The ConcatNeXt-based feature engineering model further elevated accuracy to 98.73%. Gradient-weighted class activation maps were employed to provide interpretability, offering valuable insights into model decision-making. Our proposed ConcatNeXt and nested patch-based deep feature engineering models excel in blood cell image classification, showcasing remarkable classification performances. These innovations mark significant strides in computer vision-based blood cell analysis. © The Author(s) 2024. |
| Keywords | Blood cell image classification; ConcatNeXt; Deep feature engineering; Nested patch division; Computer vision |
| Contains Sensitive Content | Does not contain sensitive content |
| ANZSRC Field of Research 2020 | 400309. Neural engineering |
| Byline Affiliations | Elazig Fethi Sekin City Hospital, Turkey |
| School of Business | |
| Firat University, Turkey | |
| National Heart Centre, Singapore | |
| Duke-NUS Medical School, Singapore | |
| School of Mathematics, Physics and Computing |
Permalink -
https://research.usq.edu.au/item/z99v4/concatnext-an-automated-blood-cell-classification-with-a-new-deep-convolutional-neural-network
Download files
29
total views12
total downloads5
views this month3
downloads this month
Export as
Related outputs
Automated hip dysplasia detection using novel FlexiLBPHOG model with ultrasound images
Key, Sefa, Kurum, Huseyin, Esmez, Omer, Hafeez-Baig, Abdul, Hajiyeva, Rena, Dogan, Sengul and Tuncer, Turker. 2025. "Automated hip dysplasia detection using novel FlexiLBPHOG model with ultrasound images." Ain Shams Engineering Journal. 16 (1). https://doi.org/10.1016/j.asej.2024.103235Artificial Intelligence-Based Suicide Prevention and Prediction: A Systematic Review (2019-2023)
Atmakuru, Anirudh, Shahini, Alen, Chakraborty, Subrata, Seoni, Silvia, Salvi, Massimo, Hafeez-Baig, Abdul, Rashid, Sadaf, Tan, Ru San, Barua, Prabal Datta, Molinari, Filippo and Acharya, U Rajendra. 2025. "Artificial Intelligence-Based Suicide Prevention and Prediction: A Systematic Review (2019-2023)." Information Fusion. 114. https://doi.org/10.1016/j.inffus.2024.102673Explainable deeply-fused nets electricity demand prediction model: Factoring climate predictors for accuracy and deeper insights with probabilistic confidence interval and point-based forecasts
Ghimire, Sujan, AL-Musaylh, Mohanad S., Nguyen-Huy, Thong, Deo, Ravinesh C., Acharya, Rajendra, Casillas-Perez, David, Yaseen, Zaher Mundher and Salcedo-sanz, Sancho. 2025. "Explainable deeply-fused nets electricity demand prediction model: Factoring climate predictors for accuracy and deeper insights with probabilistic confidence interval and point-based forecasts." Applied Energy. 378 (Part A). https://doi.org/10.1016/j.apenergy.2024.124763AttentionPoolMobileNeXt: An automated construction damage detection model based on a new convolutional neural network and deep feature engineering models
Aydin, Mehmet, Barua, Prabal Datta, Chadalavada, Sreenivasulu, Dogan, Sengul, Tuncer, Turker, Chakraborty, Subrata and Acharya, Rajendra U.. 2025. "AttentionPoolMobileNeXt: An automated construction damage detection model based on a new convolutional neural network and deep feature engineering models." Multimedia Tools and Applications. 84 (4), pp. 1821-1843. https://doi.org/10.1007/s11042-024-19163-2Directed Lobish-based explainable feature engineering model with TTPat and CWINCA for EEG artifact classification
Tuncer, Turker, Dogan, Sengul, Baygin, Mehmet, Tasci, Irem, Mungen, Bulent, Tasci, Burak, Barua, Prabal Datta and Acharya, U.R.. 2024. "Directed Lobish-based explainable feature engineering model with TTPat and CWINCA for EEG artifact classification." Knowledge-Based Systems. 305. https://doi.org/10.1016/j.knosys.2024.112555Retinal Health Screening Using Artificial Intelligence with Digital Fundus Images: A Review of the Last Decade (2012-2023)
Islam, Saad, Deo, Ravinesh C., Barua, Prabal Datta, Soar, Jeffrey, Yu, Ping and Acharya, U. Rajendra. 2024. "Retinal Health Screening Using Artificial Intelligence with Digital Fundus Images: A Review of the Last Decade (2012-2023)." IEEE Access. 12, pp. 176630-176685. https://doi.org/10.1109/ACCESS.2024.3477420Automated EEG-based language detection using directed quantum pattern technique
Dogan, Sengul, Tuncer, Turker, Barua, Prabal Datta and Acharya, U.R.. 2024. "Automated EEG-based language detection using directed quantum pattern technique." Applied Soft Computing. 167 (Part A). https://doi.org/10.1016/j.asoc.2024.112301A Novel Hybrid Model for Automatic Non-Small Cell Lung Cancer Classification Using Histopathological Images
Katar, Oguzhan, Yildirim, Ozal, Tan, Ru-San and Acharya, U Rajendra. 2024. "A Novel Hybrid Model for Automatic Non-Small Cell Lung Cancer Classification Using Histopathological Images." Diagnostics. 14 (22). https://doi.org/10.3390/diagnostics14222497Synthetic Data Generation via Generative Adversarial Networks in Healthcare: A Systematic Review of Image- and Signal-Based Studies
Akpinar, Muhammed Halil, Sengur, Abdulkadir, Salvi, Massimo, Seoni, Silvia, Faust, Oliver, Mir, Hasan, Molinari,Filippo and Acharya, U. Rajendra. 2024. "Synthetic Data Generation via Generative Adversarial Networks in Healthcare: A Systematic Review of Image- and Signal-Based Studies." IEEE Open Journal of Engineering in Medicine and Biology. 6, pp. 183-192. https://doi.org/10.1109/OJEMB.2024.3508472RECOMED: A comprehensive pharmaceutical recommendation system
Zomorodi, Mariam, Ghodsollahee, Ismail, Martin, Jennifer H, Talley, Nicholas J, Salari, Vahid, Pławiak, Paweł, Rahimi, Kazem and Acharya, U.R.. 2024. "RECOMED: A comprehensive pharmaceutical recommendation system." Artificial Intelligence in Medicine. 157. https://doi.org/10.1016/j.artmed.2024.102981Artificial intelligence in assessing cardiovascular diseases and risk factors via retinal fundus images: A review of the last decade
Abdollahi, Mirsaeed, Jafarizadeh, Ali, Ghafouri-Asbagh, Amirhosein, Sobhi, Navid, Pourmoghtader, Keysan, Pedrammehr, Siamak, Asadi, Houshyar, Tan, Ru-San, Alizadehsani, Roohallah and Acharya, U. Rajendra. 2024. "Artificial intelligence in assessing cardiovascular diseases and risk factors via retinal fundus images: A review of the last decade." WIREs Data Mining and Knowledge Discovery. 14 (6). https://doi.org/10.1002/widm.1560Early prediction of sudden cardiac death using multimodal fusion of ECG Features extracted from Hilbert–Huang and wavelet transforms with explainable vision transformer and CNN models
Telangore, Hardik, Azad, Victor, Sharma, Manish, Bhurane, Ankit, Tan, Ru San and Acharya, U. Rajendra. 2024. "Early prediction of sudden cardiac death using multimodal fusion of ECG Features extracted from Hilbert–Huang and wavelet transforms with explainable vision transformer and CNN models." Computer Methods and Programs in Biomedicine. 257. https://doi.org/10.1016/j.cmpb.2024.108455A Pragmatic Approach to Fetal Monitoring via Cardiotocography Using Feature Elimination and Hyperparameter Optimization
Hardalac, Firat, Akmal, Haad, Ayturan, Kubilay, Acharya, U. Rajendra and Tan, Ru-San. 2024. "A Pragmatic Approach to Fetal Monitoring via Cardiotocography Using Feature Elimination and Hyperparameter Optimization." Interdisciplinary Sciences: Computational Life Sciences. 16 (4), pp. 882-906. https://doi.org/10.1007/s12539-024-00647-6Automated System for the Detection of Heart Anomalies Using Phonocardiograms: A Systematic Review
Gudigar, Anjan, Raghavendra, U., Maithri, M., Samanth, Jyothi, Inamdar, Mahesh Anil, Vidhya, V., Vicnesh, Jahmunah, Prabhu, Mukund A., Tan, Ru-San, Yeong, Chai Hong, Molinari, Filippo and Acharya, U. R.. 2024. "Automated System for the Detection of Heart Anomalies Using Phonocardiograms: A Systematic Review." IEEE Access. 12, pp. 138399-138428. https://doi.org/10.1109/ACCESS.2024.3465511