An Efficient Multi-Level Convolutional Neural Network Approach for White Blood Cells Classification

Cheuque, César; Querales, Marvin; León, Roberto; Salas, Rodrigo; Torres, Romina

doi:10.3390/diagnostics12020248

Cited by 61 publications

(27 citation statements)

References 55 publications

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“…Changhun et al proposed a W-Net model in a combination of CNN with RNN with DCGANs for image synthesizing later used for WBC classification, and attained an accuracy of 97% for a 5 class dataset [ 34 ]. César Cheuque et al proposed the MLCNN detection of white blood cell Faster RCNN used to extract Region of interest later with Mobilenet based model is used to train the classification framework gained performance accuracy of 98.4% [ 35 ]. In continuation Next BCNet [ 36 ] to address the blood cell classification for three classes via transfer learning approach with ResNet18 as backbone model for learning and noted 96.78% accuracy.…”

Section: Related Workmentioning

confidence: 99%

BCNet: A Deep Learning Computer-Aided Diagnosis Framework for Human Peripheral Blood Cell Identification

Chola

Muaad²,

Heyat³

et al. 2022

Diagnostics

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Blood cells carry important information that can be used to represent a person’s current state of health. The identification of different types of blood cells in a timely and precise manner is essential to cutting the infection risks that people face on a daily basis. The BCNet is an artificial intelligence (AI)-based deep learning (DL) framework that was proposed based on the capability of transfer learning with a convolutional neural network to rapidly and automatically identify the blood cells in an eight-class identification scenario: Basophil, Eosinophil, Erythroblast, Immature Granulocytes, Lymphocyte, Monocyte, Neutrophil, and Platelet. For the purpose of establishing the dependability and viability of BCNet, exhaustive experiments consisting of five-fold cross-validation tests are carried out. Using the transfer learning strategy, we conducted in-depth comprehensive experiments on the proposed BCNet’s architecture and test it with three optimizers of ADAM, RMSprop (RMSP), and stochastic gradient descent (SGD). Meanwhile, the performance of the proposed BCNet is directly compared using the same dataset with the state-of-the-art deep learning models of DensNet, ResNet, Inception, and MobileNet. When employing the different optimizers, the BCNet framework demonstrated better classification performance with ADAM and RMSP optimizers. The best evaluation performance was achieved using the RMSP optimizer in terms of 98.51% accuracy and 96.24% F1-score. Compared with the baseline model, the BCNet clearly improved the prediction accuracy performance 1.94%, 3.33%, and 1.65% using the optimizers of ADAM, RMSP, and SGD, respectively. The proposed BCNet model outperformed the AI models of DenseNet, ResNet, Inception, and MobileNet in terms of the testing time of a single blood cell image by 10.98, 4.26, 2.03, and 0.21 msec. In comparison to the most recent deep learning models, the BCNet model could be able to generate encouraging outcomes. It is essential for the advancement of healthcare facilities to have such a recognition rate improving the detection performance of the blood cells.

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Section: Related Workmentioning

confidence: 99%

BCNet: A Deep Learning Computer-Aided Diagnosis Framework for Human Peripheral Blood Cell Identification

Chola

Muaad²,

Heyat³

et al. 2022

Diagnostics

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“…They deployed the proposed approach on the Kaggle WBC images dataset and achieved significant accuracy. The study [ 16 ] proposed a multi-level CNN model for the WBC classification for four types of cell classification. At the first level, Faster R-CNN is applied for the detection of the region of interest while at the second level, CNN-based architecture MobileNet is used for cell-type classification.…”

Section: Related Workmentioning

confidence: 99%

White Blood Cell Classification Using Texture and RGB Features of Oversampled Microscopic Images

et al. 2022

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White blood cell (WBC) type classification is a task of significant importance for diagnosis using microscopic images of WBC, which develop immunity to fight against infections and foreign substances. WBCs consist of different types, and abnormalities in a type of WBC may potentially represent a disease such as leukemia. Existing studies are limited by low accuracy and overrated performance, often caused by model overfit due to an imbalanced dataset. Additionally, many studies consider a lower number of WBC types, and the accuracy is exaggerated. This study presents a hybrid feature set of selective features and synthetic minority oversampling technique-based resampling to mitigate the influence of the above-mentioned problems. Furthermore, machine learning models are adopted for being less computationally complex, requiring less data for training, and providing robust results. Experiments are performed using both machine- and deep learning models for performance comparison using the original dataset, augmented dataset, and oversampled dataset to analyze the performances of the models. The results suggest that a hybrid feature set of both texture and RGB features from microscopic images, selected using Chi2, produces a high accuracy of 0.97 with random forest. Performance appraisal using k-fold cross-validation and comparison with existing state-of-the-art studies shows that the proposed approach outperforms existing studies regarding the obtained accuracy and computational complexity.

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“…Table 1 summarizes the literature in chronological order to provide a better understanding of the current status of the WBC classification methods along with the model architectures employed. As can be seen from the table, most previous methods highly relied on CNN-based architectures, such as AlexNet, MobileNet, etc., due to their efficiency in analyzing images, while these approaches have shown good performance in the WBC classification [8,24,28], extracting the features associated with distinct regions of the cell is still difficult to achieve. There exist subtle discrepancies among different cell types, which tend to be retained in textural information of shallow features.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, researchers in the community have devised automatic yet faster approaches for analysis of leukocytes leveraging computer vision techniques [5][6][7][8][9]. Given the recent advancement of machine learning and computer vision, several approaches have been proposed for leukocyte classification and segmentation, ranging from more conventional machine learning models such as support vector machine [10] and Naïve Bayesian [11] to more advanced deep learning methods [12,13].…”

Section: Introductionmentioning

confidence: 99%

White Blood Cell Classification Using Multi-Attention Data Augmentation and Regularization

Bayat

Davey

Coathup

et al. 2022

BDCC

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Accurate and robust human immune system assessment through white blood cell evaluation require computer-aided tools with pathologist-level accuracy. This work presents a multi-attention leukocytes subtype classification method by leveraging fine-grained and spatial locality attributes of white blood cell. The proposed framework comprises three main components: texture-aware/attention map generation blocks, attention regularization, and attention-based data augmentation. The developed framework is applicable to general CNN-based architectures and enhances decision making by paying specific attention to the discriminative regions of a white blood cell. The performance of the proposed method/model was evaluated through an extensive set of experiments and validation. The obtained results demonstrate the superior performance of the model achieving 99.69 % accuracy compared to other state-of-the-art approaches. The proposed model is a good alternative and complementary to existing computer diagnosis tools to assist pathologists in evaluating white blood cells from blood smear images.

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An Efficient Multi-Level Convolutional Neural Network Approach for White Blood Cells Classification

Cited by 61 publications

References 55 publications

BCNet: A Deep Learning Computer-Aided Diagnosis Framework for Human Peripheral Blood Cell Identification

BCNet: A Deep Learning Computer-Aided Diagnosis Framework for Human Peripheral Blood Cell Identification

White Blood Cell Classification Using Texture and RGB Features of Oversampled Microscopic Images

White Blood Cell Classification Using Multi-Attention Data Augmentation and Regularization

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