White blood cell subtype detection and classification

• URL: http://arxiv.org/abs/2108.04614v1
• Date: Tue, 10 Aug 2021 11:55:52 GMT
• Title: White blood cell subtype detection and classification
• Authors: Nalla Praveen, Narinder Singh Punn, Sanjay Kumar Sonbhadra, Sonali Agarwal
• Abstract summary: The classification of the white blood cells plays an important part in the medical diagnosis. The current procedures to identify the white blood cell subtype is more time taking and error-prone. The proposed work is found to detect the white blood cell with 99.2% accuracy and classify with 90% accuracy.
• Score: 0.9176056742068814
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Machine learning has endless applications in the health care industry. White blood cell classification is one of the interesting and promising area of research. The classification of the white blood cells plays an important part in the medical diagnosis. In practise white blood cell classification is performed by the haematologist by taking a small smear of blood and careful examination under the microscope. The current procedures to identify the white blood cell subtype is more time taking and error-prone. The computer aided detection and diagnosis of the white blood cells tend to avoid the human error and reduce the time taken to classify the white blood cells. In the recent years several deep learning approaches have been developed in the context of classification of the white blood cells that are able to identify but are unable to localize the positions of white blood cells in the blood cell image. Following this, the present research proposes to utilize YOLOv3 object detection technique to localize and classify the white blood cells with bounding boxes. With exhaustive experimental analysis, the proposed work is found to detect the white blood cell with 99.2% accuracy and classify with 90% accuracy.

Related papers

• Automatic Classification of White Blood Cell Images using Convolutional Neural Network [0.0]
  Human immune system contains white blood cells (WBC) that are good indicator of many diseases like bacterial infections, AIDS, cancer, spleen, etc. Traditionally in laboratories, pathologists and hematologists analyze these blood cells through microscope and then classify them manually. In this paper, first we have used different CNN pre-train models such as ResNet-50, InceptionV3, VGG16 and MobileNetV2 to automatically classify the white blood cells. Inspired by these architectures, a framework has been proposed to automatically categorize the four kinds of white blood cells with increased accuracy.
  arXiv  Detail & Related papers  (2024-09-19T16:39:46Z)
• Analysis of Modern Computer Vision Models for Blood Cell Classification [49.1574468325115]
  This study uses state-of-the-art architectures, including MaxVit, EfficientVit, EfficientNet, EfficientNetV2, and MobileNetV3 to achieve rapid and accurate results. Our approach not only addresses the speed and accuracy concerns of traditional techniques but also explores the applicability of innovative deep learning models in hematological analysis.
  arXiv  Detail & Related papers  (2024-06-30T16:49:29Z)
• Neural Cellular Automata for Lightweight, Robust and Explainable Classification of White Blood Cell Images [40.347953893940044]
  We introduce a novel approach for white blood cell classification based on neural cellular automata (NCA) Our NCA-based method is significantly smaller in terms of parameters and exhibits robustness to domain shifts. Our results demonstrate that NCA can be used for image classification, and they address key challenges of conventional methods.
  arXiv  Detail & Related papers  (2024-04-08T14:59:53Z)
• Single-Cell Deep Clustering Method Assisted by Exogenous Gene Information: A Novel Approach to Identifying Cell Types [50.55583697209676]
  We develop an attention-enhanced graph autoencoder, which is designed to efficiently capture the topological features between cells. During the clustering process, we integrated both sets of information and reconstructed the features of both cells and genes to generate a discriminative representation. This research offers enhanced insights into the characteristics and distribution of cells, thereby laying the groundwork for early diagnosis and treatment of diseases.
  arXiv  Detail & Related papers  (2023-11-28T09:14:55Z)
• A Fully Unsupervised Instance Segmentation Technique for White Blood Cell Images [0.4532517021515834]
  White blood cells, also known as leukocytes are group of heterogeneously nucleated cells which act as salient immune system cells. Leukocytes kill the bacteria, virus and other kind of pathogens which invade human body through phagocytosis. Detection of a white blood cell count can reveal camouflaged infections and warn doctors about chronic medical conditions.
  arXiv  Detail & Related papers  (2023-06-26T17:44:36Z)
• Pixel-Level Explanation of Multiple Instance Learning Models in Biomedical Single Cell Images [52.527733226555206]
  We investigate the use of four attribution methods to explain a multiple instance learning models. We study two datasets of acute myeloid leukemia with over 100 000 single cell images. We compare attribution maps with the annotations of a medical expert to see how the model's decision-making differs from the human standard.
  arXiv  Detail & Related papers  (2023-03-15T14:00:11Z)
• A survey on automated detection and classification of acute leukemia and WBCs in microscopic blood cells [6.117084972237769]
  Leukemia (blood cancer) is an unusual spread of White Blood Cells or Leukocytes (WBCs) in the bone marrow and blood. Traditional machine learning and deep learning techniques are practical roadmaps that increase the accuracy and speed in diagnosing and classifying medical images. This paper provides a comprehensive analysis of the detection and classification of acute leukemia and WBCs in the microscopic blood cells.
  arXiv  Detail & Related papers  (2023-03-07T14:26:08Z)
• Lymphocyte Classification in Hyperspectral Images of Ovarian Cancer Tissue Biopsy Samples [94.37521840642141]
  We present a machine learning pipeline to segment white blood cell pixels in hyperspectral images of biopsy cores. These cells are clinically important for diagnosis, but some prior work has struggled to incorporate them due to difficulty obtaining precise pixel labels.
  arXiv  Detail & Related papers  (2022-03-23T00:58:27Z)
• White blood cell classification [3.386401892906348]
  We propose an adaptive threshold segmentation method to deal with blood smears images with non-uniform color and uneven illumination. A feature selection algorithm based on classification and regression trees (CART) is designed. The proposed methodology achieves 99.76% classification accuracy, which well demonstrates its effectiveness.
  arXiv  Detail & Related papers  (2020-08-17T09:48:12Z)
• Attention based Multiple Instance Learning for Classification of Blood Cell Disorders [38.086308180994976]
  We propose an attention based multiple instance learning method to classify blood samples of patients suffering from blood cell disorders. With the features extracted for each cell, a multiple instance learning method classifies patient samples into one out of four blood cell disorders. The attention mechanism provides a measure of the contribution of each cell to the overall classification and significantly improves the network's classification accuracy as well as its interpretability for the medical expert.
  arXiv  Detail & Related papers  (2020-07-22T19:29:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.