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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.<\/jats:p>\n          <jats:p>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.<\/jats:p>\n          <jats:p>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.<\/jats:p>\n          <jats:p>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.<\/jats:p>","DOI":"10.1007\/s11042-024-19899-x","type":"journal-article","created":{"date-parts":[[2024,8,2]],"date-time":"2024-08-02T04:12:33Z","timestamp":1722571953000},"page":"22231-22249","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["ConcatNeXt: An automated blood cell classification with a new deep convolutional neural network"],"prefix":"10.1007","volume":"84","author":[{"given":"Mehmet","family":"Erten","sequence":"first","affiliation":[]},{"given":"Prabal Datta","family":"Barua","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9677-5684","authenticated-orcid":false,"given":"Sengul","family":"Dogan","sequence":"additional","affiliation":[]},{"given":"Turker","family":"Tuncer","sequence":"additional","affiliation":[]},{"given":"Ru-San","family":"Tan","sequence":"additional","affiliation":[]},{"given":"U. 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