Classification of RBC and WBC in Noisy Microscopic Images of Blood Smear

Ghosh, Sayantari; Bhattacharya, Sumanta

doi:10.1007/978-981-32-9453-0_20

Cited by 6 publications

(5 citation statements)

References 6 publications

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“…The classification of blood cells has been a subject of interest in the last few decades. This interest seems to have been considerably influenced by the general growth of machine and deep learning for unconventional tasks such as classifying chest X-rays [ 13 – 15 ], red blood cell [ 16 , 17 ], segmenting medical images [ 18 – 21 ], breast cancer determination [ 22 , 23 ], and Alzheimer's disease [ 24 , 25 ]. For instance, the work [ 26 ] proposed the identification of the red blood cell, white blood cell, and platelet using the popular YOLO object detection algorithm and deep neural networks for classification with interesting results.…”

Section: Related Workmentioning

confidence: 99%

Improved Classification of White Blood Cells with the Generative Adversarial Network and Deep Convolutional Neural Network

Almezhghwi

Serte

2020

Computational Intelligence and Neuroscience

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White blood cells (leukocytes) are a very important component of the blood that forms the immune system, which is responsible for fighting foreign elements. The five types of white blood cells include neutrophils, eosinophils, lymphocytes, monocytes, and basophils, where each type constitutes a different proportion and performs specific functions. Being able to classify and, therefore, count these different constituents is critical for assessing the health of patients and infection risks. Generally, laboratory experiments are used for determining the type of a white blood cell. The staining process and manual evaluation of acquired images under the microscope are tedious and subject to human errors. Moreover, a major challenge is the unavailability of training data that cover the morphological variations of white blood cells so that trained classifiers can generalize well. As such, this paper investigates image transformation operations and generative adversarial networks (GAN) for data augmentation and state-of-the-art deep neural networks (i.e., VGG-16, ResNet, and DenseNet) for the classification of white blood cells into the five types. Furthermore, we explore initializing the DNNs’ weights randomly or using weights pretrained on the CIFAR-100 dataset. In contrast to other works that require advanced image preprocessing and manual feature extraction before classification, our method works directly with the acquired images. The results of extensive experiments show that the proposed method can successfully classify white blood cells. The best DNN model, DenseNet-169, yields a validation accuracy of 98.8%. Particularly, we find that the proposed approach outperforms other methods that rely on sophisticated image processing and manual feature engineering.

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Section: Related Workmentioning

confidence: 99%

Improved Classification of White Blood Cells with the Generative Adversarial Network and Deep Convolutional Neural Network

Almezhghwi

Serte

2020

Computational Intelligence and Neuroscience

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“…In addition to this, it has accomplished a level of ACC that is on par with that which Habibzadeh et al. ( 39 ), Rao and Rao ( 24 ), and Ghosh and Bhattacharya ( 26 ) have accomplished.…”

Section: Resultsmentioning

confidence: 69%

“…The differences between the suggested method and the state-of-the-art methods that are 29) when applied to the BCCD dataset. In addition to this, it has accomplished a level of ACC that is on par with that which Habibzadeh et al (39), Rao and Rao (24), and Ghosh and Bhattacharya (26) have accomplished.…”

Section: Comparisons With Other Modelsmentioning

confidence: 69%

“…A good classifier is immune to a wide-ranging variation. CNN as a framework well-established for image data can discriminate relevant minor features in the image while it is invariant to unrelated large variations in the image ( 26 ). For image datasets, augmentation can be done by modifying the images a few pixels to improve the generalization ability and avoid overfitting.…”

Section: Methodsmentioning

confidence: 99%

“…They have attained an ACC of 96.9% on the benchmarking dataset, LISC. Ghosh and Bhattacharya ( 26 ) came up with two distinct models of CNNs that improve and categorize input images of blood cells. On the BCCD benchmarking dataset, they have achieved an ACC of 98.4%.…”

Section: Literature Reviewmentioning

confidence: 99%

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Using deep DenseNet with cyclical learning rate to classify leukocytes for leukemia identification

Houssein,

Mohamed,

Abdel Samee

et al. 2023

Front. Oncol.

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BackgroundThe examination, counting, and classification of white blood cells (WBCs), also known as leukocytes, are essential processes in the diagnosis of many disorders, including leukemia, a kind of blood cancer characterized by the uncontrolled proliferation of carcinogenic leukocytes in the marrow of the bone. Blood smears can be chemically or microscopically studied to better understand hematological diseases and blood disorders. Detecting, identifying, and categorizing the many blood cell types are essential for disease diagnosis and therapy planning. A theoretical and practical issue. However, methods based on deep learning (DL) have greatly helped blood cell classification.Materials and MethodsImages of blood cells in a microscopic smear were collected from GitHub, a public source that uses the MIT license. An end-to-end computer-aided diagnosis (CAD) system for leukocytes has been created and implemented as part of this study. The introduced system comprises image preprocessing and enhancement, image segmentation, feature extraction and selection, and WBC classification. By combining the DenseNet-161 and the cyclical learning rate (CLR), we contribute an approach that speeds up hyperparameter optimization. We also offer the one-cycle technique to rapidly optimize all hyperparameters of DL models to boost training performance.ResultsThe dataset has been split into two sets: approximately 80% of the data (9,966 images) for the training set and 20% (2,487 images) for the validation set. The validation set has 623, 620, 620, and 624 eosinophil, lymphocyte, monocyte, and neutrophil images, whereas the training set has 2,497, 2,483, 2,487, and 2,499, respectively. The suggested method has 100% accuracy on the training set of images and 99.8% accuracy on the testing set.ConclusionUsing a combination of the recently developed pretrained convolutional neural network (CNN), DenseNet, and the one fit cycle policy, this study describes a technique of training for the classification of WBCs for leukemia detection. The proposed method is more accurate compared to the state of the art.

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Microscopic image segmentation approach based on modified affinity propagation-based clustering

Chakraborty,

Mali

2024

Multimed Tools Appl

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Classification of RBC and WBC in Noisy Microscopic Images of Blood Smear

Cited by 6 publications

References 6 publications

Improved Classification of White Blood Cells with the Generative Adversarial Network and Deep Convolutional Neural Network

Improved Classification of White Blood Cells with the Generative Adversarial Network and Deep Convolutional Neural Network

Using deep DenseNet with cyclical learning rate to classify leukocytes for leukemia identification

Microscopic image segmentation approach based on modified affinity propagation-based clustering

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