White Blood Cell Classification Using Convolutional Neural Network

NAHZAT, Shamriz; Bozkurt, Ferhat; Yağanoğlu, Mete

doi:10.53525/jster.1018213

Cited by 10 publications

(6 citation statements)

References 16 publications

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“…The performance of the proposed method was evaluated using the Kaggle dataset, resulting in an overall accuracy of 98.55%. Nahzat et al [36] aimed to develop a CNN-based model for the classification of WBCs. They used images of WBCs from the Kaggle dataset to train and evaluate their proposed model, testing it with various optimizers to determine the best performance.…”

Section: Discussionmentioning

confidence: 99%

Automatic Classification of White Blood Cells Using Pre-Trained Deep Models

KATAR¹,

Fırat²

2022

Sakarya University Journal of Computer and Information Sciences

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White blood cells (WBCs), which are part of the immune system, help our body fight infections and other diseases. Certain diseases can cause our body to produce fewer WBCs than it needs. For this reason, WBCs are of great importance in the field of medical imaging. Artificial intelligence-based computer systems can assist experts in the analysis of WBCs. In this study, an approach is proposed for the automatic classification of WBCs over five different classes using a pre-trained model. ResNet-50, VGG-19, and MobileNet-V3-Small pre-trained models were trained with ImageNet weights. In the training, validation, and testing processes of the models, a public dataset containing 16,633 images and not having an even class distribution was used. While the ResNet-50 model reached 98.79% accuracy, the VGG-19 model reached 98.19% accuracy, the MobileNet-V3-Small model reached the highest accuracy rate with 98.86%. When the predictions of the MobileNet-V3-Small model are examined, it is seen that it is not affected by class dominance and can classify even the least sampled class images in the dataset correctly. WBCs were classified with high accuracy using the proposed pre-trained deep learning models. Experts can effectively use the proposed approach in the process of analyzing WBCs.

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Section: Discussionmentioning

confidence: 99%

Automatic Classification of White Blood Cells Using Pre-Trained Deep Models

KATAR¹,

Fırat²

2022

Sakarya University Journal of Computer and Information Sciences

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“…Convolution is divided into two independent layers by MobileNetV2. Each channel is individually subjected to a depth-wise convolution in the first layer, known as the depth-wise convolution [37]. The outputs of the depth-wise convolution are combined in the second layer, referred to as the point-wise convolution, using a 1 × 1 convolution.…”

Section: Fine-tuned Mobilenetv2mentioning

confidence: 99%

“…The Adamax optimizer is a variation of the Adam optimizer, which excels at solving problems with huge parameter spaces and sparse gradients [36]. An optimization approach called Adaptive Gradient (AdaGrad) adjusts the learning rate of each parameter based on previous gradients [37]. The fundamental idea underlying AdaGrad is to provide each parameter with a unique learning rate dependent on the size of its previous gradient.…”

Section: Different Optimizers Employed For Best Fine-tuned Tl Modelmentioning

confidence: 99%

“…It is a straightforward and often employed optimizer, although it can converge slowly and is sensitive to the learning rate. The optimization approach Root Mean Square Propagation (RMSprop) overcomes AdaGrad's drawbacks of aggressive and monotonically declining learning rates [37]. It is intended to enhance optimization processes' convergence and stability, particularly for deep learning models.…”

Section: Different Optimizers Employed For Best Fine-tuned Tl Modelmentioning

confidence: 99%

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An Intelligent Attention-Based Transfer Learning Model for Accurate Differentiation of Bone Marrow Stains to Diagnose Hematological Disorder

Alshahrani,

Sharma,

Anand

et al. 2023

Life

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Bone marrow (BM) is an essential part of the hematopoietic system, which generates all of the body’s blood cells and maintains the body’s overall health and immune system. The classification of bone marrow cells is pivotal in both clinical and research settings because many hematological diseases, such as leukemia, myelodysplastic syndromes, and anemias, are diagnosed based on specific abnormalities in the number, type, or morphology of bone marrow cells. There is a requirement for developing a robust deep-learning algorithm to diagnose bone marrow cells to keep a close check on them. This study proposes a framework for categorizing bone marrow cells into seven classes. In the proposed framework, five transfer learning models—DenseNet121, EfficientNetB5, ResNet50, Xception, and MobileNetV2—are implemented into the bone marrow dataset to classify them into seven classes. The best-performing DenseNet121 model was fine-tuned by adding one batch-normalization layer, one dropout layer, and two dense layers. The proposed fine-tuned DenseNet121 model was optimized using several optimizers, such as AdaGrad, AdaDelta, Adamax, RMSprop, and SGD, along with different batch sizes of 16, 32, 64, and 128. The fine-tuned DenseNet121 model was integrated with an attention mechanism to improve its performance by allowing the model to focus on the most relevant features or regions of the image, which can be particularly beneficial in medical imaging, where certain regions might have critical diagnostic information. The proposed fine-tuned and integrated DenseNet121 achieved the highest accuracy, with a training success rate of 99.97% and a testing success rate of 97.01%. The key hyperparameters, such as batch size, number of epochs, and different optimizers, were all considered for optimizing these pre-trained models to select the best model. This study will help in medical research to effectively classify the BM cells to prevent diseases like leukemia.

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“…Nahzat et al [19] designed a CNN-based model using the Kaggle BCCD dataset, achieving competitive results. They also focused on five WBC types.…”

Section: Related Workmentioning

confidence: 99%

Blood Cell Revolution: Unveiling 11 Distinct Types with ‘Naturalize’ Augmentation

Abou Ali,

Dornaika,

Arganda-Carreras

2023

Algorithms

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Artificial intelligence (AI) has emerged as a cutting-edge tool, simultaneously accelerating, securing, and enhancing the diagnosis and treatment of patients. An exemplification of this capability is evident in the analysis of peripheral blood smears (PBS). In university medical centers, hematologists routinely examine hundreds of PBS slides daily to validate or correct outcomes produced by advanced hematology analyzers assessing samples from potentially problematic patients. This process may logically lead to erroneous PBC readings, posing risks to patient health. AI functions as a transformative tool, significantly improving the accuracy and precision of readings and diagnoses. This study reshapes the parameters of blood cell classification, harnessing the capabilities of AI and broadening the scope from 5 to 11 specific blood cell categories with the challenging 11-class PBC dataset. This transformation facilitates a more profound exploration of blood cell diversity, surpassing prior constraints 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.

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White Blood Cell Classification Using Convolutional Neural Network

Cited by 10 publications

References 16 publications

Automatic Classification of White Blood Cells Using Pre-Trained Deep Models

Automatic Classification of White Blood Cells Using Pre-Trained Deep Models

An Intelligent Attention-Based Transfer Learning Model for Accurate Differentiation of Bone Marrow Stains to Diagnose Hematological Disorder

Blood Cell Revolution: Unveiling 11 Distinct Types with ‘Naturalize’ Augmentation

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