Machine learning approach of automatic identification and counting of blood cells

Alam, Mohammad Mahmudul; Islam, Mohammad Tariqul

doi:10.1049/htl.2018.5098

Cited by 119 publications

(54 citation statements)

References 19 publications

(38 reference statements)

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“…For instance, in otolaryngology, CNNs can be used to help primary care physicians manage patients’ ears, nose, and throat 40 , through mountable devices attached to smartphones 41 . Hematology and serology can benefit from microscope-integrated AIs 42 that diagnose common conditions 43 or count blood cells of various types 44 —repetitive tasks that are easy to augment with CNNs. AI in gastroenterology has demonstrated stunning capabilities.…”

Section: Medical Imagingmentioning

confidence: 99%

Deep learning-enabled medical computer vision

et al. 2021

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A decade of unprecedented progress in artificial intelligence (AI) has demonstrated the potential for many fields—including medicine—to benefit from the insights that AI techniques can extract from data. Here we survey recent progress in the development of modern computer vision techniques—powered by deep learning—for medical applications, focusing on medical imaging, medical video, and clinical deployment. We start by briefly summarizing a decade of progress in convolutional neural networks, including the vision tasks they enable, in the context of healthcare. Next, we discuss several example medical imaging applications that stand to benefit—including cardiology, pathology, dermatology, ophthalmology–and propose new avenues for continued work. We then expand into general medical video, highlighting ways in which clinical workflows can integrate computer vision to enhance care. Finally, we discuss the challenges and hurdles required for real-world clinical deployment of these technologies.

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Section: Medical Imagingmentioning

confidence: 99%

Deep learning-enabled medical computer vision

et al. 2021

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“…is 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][14][15], red blood cell [16,17], segmenting medical images [18][19][20][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. e automatic classification of blood cells is commonly achieved using advanced image preprocessing and feature extraction.…”

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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“…Deep learning-based models require a large amount of training samples to improve generalization capability. In the field of clinical medicine, collecting a large number of annotated samples is time-consuming, labor-intensive and subjec- tive [43]- [45]. To make up for the shortfall in the amount of dataset, data augmentation was introduced during the experiment.…”

Section: ) Data Augmentationmentioning

confidence: 99%

Color Space Transformation and Multi-Class Weighted Loss for Adhesive White Blood Cell Segmentation

Zhao

et al. 2020

IEEE Access

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White blood cells (WBCs) are the cells of immune system, protecting against infective diseases and invasion of viruses and bacteria. Their aberrant number, both abnormal increase and decrease, is a sign of an ongoing pathology, a precise evaluation of their number is of the utmost importance as the first step of assessing a potential disease. In blood cell microscopic images, since red blood cells and platelets are similar in color with WBCs, and WBCs are partially adhesive, WBC segmentation for counting is often not resulting in a good performance. Therefore, in this work, a color space transformation is proposed to filter out red blood cells and platelets, which is transforming the blood cell microscopic images of patients with acute lymphoblastic leukemia from RGB color space to HSV to detect and extract WBCs. For precisely segmenting adhesive WBCs in extraction results, we set cell border to the third class, in addition to foreground and background. A weighted cross-entropy loss function based on class weight and distance transformation weight enhanced U-Net to learn cell border features. Our results showed that the method proposed in this paper for WBC segmentation using the data set ALL_IDB1 could achieve an accuracy of 97.92%.

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Machine learning approach of automatic identification and counting of blood cells

Cited by 119 publications

References 19 publications

Deep learning-enabled medical computer vision

Deep learning-enabled medical computer vision

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

Color Space Transformation and Multi-Class Weighted Loss for Adhesive White Blood Cell Segmentation

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