WBC image classification and generative models based on convolutional neural network

Jung, Changhun; Abuhamad, Mohammed; Mohaisen, Aziz; Han, Kyungja; Nyang, DaeHun

doi:10.1186/s12880-022-00818-1

Cited by 27 publications

(8 citation statements)

References 66 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The proposed method achieves a binary classification accuracy of 96.17%. In the study mentioned in the reference Jung et al ( 2022 ), the authors proposed a custom CNN model for WBC classification for leukemia detection. The authors first created a synthetic dataset of WBC images using generative adversarial networks and then performed transfer learning of the proposed CNN for classification.…”

Section: Literature Reviewmentioning

confidence: 99%

ALL classification using neural ensemble and memetic deep feature optimization

Awais,

Ahmad,

Kausar

et al. 2024

Front. Artif. Intell.

View full text Add to dashboard Cite

Acute lymphoblastic leukemia (ALL) is a fatal blood disorder characterized by the excessive proliferation of immature white blood cells, originating in the bone marrow. An effective prognosis and treatment of ALL calls for its accurate and timely detection. Deep convolutional neural networks (CNNs) have shown promising results in digital pathology. However, they face challenges in classifying different subtypes of leukemia due to their subtle morphological differences. This study proposes an improved pipeline for binary detection and sub-type classification of ALL from blood smear images. At first, a customized, 88 layers deep CNN is proposed and trained using transfer learning along with GoogleNet CNN to create an ensemble of features. Furthermore, this study models the feature selection problem as a combinatorial optimization problem and proposes a memetic version of binary whale optimization algorithm, incorporating Differential Evolution-based local search method to enhance the exploration and exploitation of feature search space. The proposed approach is validated using publicly available standard datasets containing peripheral blood smear images of various classes of ALL. An overall best average accuracy of 99.15% is achieved for binary classification of ALL with an 85% decrease in the feature vector, together with 99% precision and 98.8% sensitivity. For B-ALL sub-type classification, the best accuracy of 98.69% is attained with 98.7% precision and 99.57% specificity. The proposed methodology shows better performance metrics as compared with several existing studies.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

ALL classification using neural ensemble and memetic deep feature optimization

Awais,

Ahmad,

Kausar

et al. 2024

Front. Artif. Intell.

View full text Add to dashboard Cite

show abstract

“…Segmentation was achieved using region-based segmentation, K-means Zack Algorithm, Morphological operation, gradient magnitude, and watershed transform with a supervised machine learning approach for classification. In Jung et al (2022) , Othman, Mohammed & Ali (2017) , authors introduced feed-forward back-propagation neural network-based and generative adversarial networks-based techniques for WBCs classification. They got a 96% classification accuracy rate of white blood cells with 16 selected features.…”

Section: Literature Reviewmentioning

confidence: 99%

Blood cell image segmentation and classification: a systematic review

Shahzad,

Ali,

Shirazi

et al. 2024

PeerJ Computer Science

View full text Add to dashboard Cite

Background Blood diseases such as leukemia, anemia, lymphoma, and thalassemia are hematological disorders that relate to abnormalities in the morphology and concentration of blood elements, specifically white blood cells (WBC) and red blood cells (RBC). Accurate and efficient diagnosis of these conditions significantly depends on the expertise of hematologists and pathologists. To assist the pathologist in the diagnostic process, there has been growing interest in utilizing computer-aided diagnostic (CAD) techniques, particularly those using medical image processing and machine learning algorithms. Previous surveys in this domain have been narrowly focused, often only addressing specific areas like segmentation or classification but lacking a holistic view like segmentation, classification, feature extraction, dataset utilization, evaluation matrices, etc. Methodology This survey aims to provide a comprehensive and systematic review of existing literature and research work in the field of blood image analysis using deep learning techniques. It particularly focuses on medical image processing techniques and deep learning algorithms that excel in the morphological characterization of WBCs and RBCs. The review is structured to cover four main areas: segmentation techniques, classification methodologies, descriptive feature selection, evaluation parameters, and dataset selection for the analysis of WBCs and RBCs. Results Our analysis reveals several interesting trends and preferences among researchers. Regarding dataset selection, approximately 50% of research related to WBC segmentation and 60% for RBC segmentation opted for manually obtaining images rather than using a predefined dataset. When it comes to classification, 45% of the previous work on WBCs chose the ALL-IDB dataset, while a significant 73% of researchers focused on RBC classification decided to manually obtain images from medical institutions instead of utilizing predefined datasets. In terms of feature selection for classification, morphological features were the most popular, being chosen in 55% and 80% of studies related to WBC and RBC classification, respectively. Conclusion The diagnostic accuracy for blood-related diseases like leukemia, anemia, lymphoma, and thalassemia can be significantly enhanced through the effective use of CAD techniques, which have evolved considerably in recent years. This survey provides a broad and in-depth review of the techniques being employed, from image segmentation to classification, feature selection, utilization of evaluation matrices, and dataset selection. The inconsistency in dataset selection suggests a need for standardized, high-quality datasets to strengthen the diagnostic capabilities of these techniques further. Additionally, the popularity of morphological features indicates that future research could further explore and innovate in this direction.

show abstract

“…The combinatory approach of machine learning and the deep learning-based approach for the classification of WBC images were able to achieve 97.57% accuracy [ 33 ]. 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 ].…”

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

View full text Add to dashboard Cite

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.

show abstract

WBC image classification and generative models based on convolutional neural network

Cited by 27 publications

References 66 publications

ALL classification using neural ensemble and memetic deep feature optimization

ALL classification using neural ensemble and memetic deep feature optimization

Blood cell image segmentation and classification: a systematic review

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

Contact Info

Product

Resources

About