Abou Ali, M.; Dornaika, F.; Arganda-Carreras, I. Blood Cell Revolution: Unveiling 11 Distinct Types with ‘Naturalize’ Augmentation. Algorithms2023, 16, 562.
Abou Ali, M.; Dornaika, F.; Arganda-Carreras, I. Blood Cell Revolution: Unveiling 11 Distinct Types with ‘Naturalize’ Augmentation. Algorithms 2023, 16, 562.
Abou Ali, M.; Dornaika, F.; Arganda-Carreras, I. Blood Cell Revolution: Unveiling 11 Distinct Types with ‘Naturalize’ Augmentation. Algorithms2023, 16, 562.
Abou Ali, M.; Dornaika, F.; Arganda-Carreras, I. Blood Cell Revolution: Unveiling 11 Distinct Types with ‘Naturalize’ Augmentation. Algorithms 2023, 16, 562.
Abstract
In this paper, we redefine the boundaries of blood cell classification, expanding from 5 to 11 distinct blood cell types using the challenging 11-class PBC dataset. This shift enables a deeper understanding of blood cell diversity, overcoming previous limitations in medical image analysis. Our approach combines state-of-the-art deep learning techniques, including pre-trained ConvNets, ViTb16 models, and custom CNN architectures. We employ transfer learning, fine-tuning, and ensemble strategies, such as CBAM and Averaging ensembles, to achieve unprecedented accuracy and interpretability. Our fully fine-tuned EfficientNetV2 B0 model sets a new standard, with a macro-average precision, recall, and F1-score of 91%, 90%, and 90%, respectively, and an average accuracy of 93%. This breakthrough underscores the transformative potential of 11-class blood cell classification for more precise medical diagnoses. Moreover, our groundbreaking "Naturalize" augmentation technique produces remarkable results. The 2K-PBC dataset generated with "Naturalize" boasts a macro-average precision, recall, and F1-score of 97%, along with an average accuracy of 96% when leveraging the fully fine-tuned EfficientNetV2 B0 model. This innovation not only elevates classification performance but also addresses data scarcity and bias in medical deep learning. Our research marks a paradigm shift in blood cell classification, enabling more nuanced and insightful medical analyses. The "Naturalize" technique’s impact extends beyond blood cell classification, emphasizing the vital role of diverse and comprehensive datasets in advancing healthcare applications through deep learning.
Keywords
Convolutional Neural Net (CNN); Vision Transformer (ViT); ImageNet Models; Transfer Learning (TL); Machine Learning (ML); Deep Learning (DP); Blood Cell Classification, Peripheral Blood Cell (PBC), CBAM, Naturalize
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
