Classification of All Blood Cell Images using ML and DL Models

• URL: http://arxiv.org/abs/2308.06300v3
• Date: Mon, 07 Oct 2024 09:48:14 GMT
• Title: Classification of All Blood Cell Images using ML and DL Models
• Authors: Rabia Asghar, Sanjay Kumar, Paul Hynds, Abeera Mahfooz,
• Abstract summary: Human blood primarily comprises plasma, red blood cells, white blood cells, and platelets. It plays a vital role in transporting nutrients to different organs, where it stores essential health-related data about the human body. Blood analysis can help physicians assess an individual's physiological condition.
• Score: 7.737213476933511
• License:
• Abstract: Human blood primarily comprises plasma, red blood cells, white blood cells, and platelets. It plays a vital role in transporting nutrients to different organs, where it stores essential health-related data about the human body. Blood cells are utilized to defend the body against diverse infections, including fungi, viruses, and bacteria. Hence, blood analysis can help physicians assess an individual's physiological condition. Blood cells have been sub-classified into eight groups: Neutrophils, eosinophils, basophils, lymphocytes, monocytes, immature granulocytes (promyelocytes, myelocytes, and metamyelocytes), erythroblasts, and platelets or thrombocytes on the basis of their nucleus, shape, and cytoplasm. Traditionally, pathologists and hematologists in laboratories have examined these blood cells using a microscope before manually classifying them. The manual approach is slower and more prone to human error. Therefore, it is essential to automate this process. In our paper, transfer learning with CNN pre-trained models. VGG16, VGG19, ResNet-50, ResNet-101, ResNet-152, InceptionV3, MobileNetV2, and DenseNet-20 applied to the PBC dataset's normal DIB. The overall accuracy achieved with these models lies between 91.375 and 94.72%. Hence, inspired by these pre-trained architectures, a model has been proposed to automatically classify the ten types of blood cells with increased accuracy. A novel CNN-based framework has been presented to improve accuracy. The proposed CNN model has been tested on the PBC dataset normal DIB. The outcomes of the experiments demonstrate that our CNN-based framework designed for blood cell classification attains an accuracy of 99.91% on the PBC dataset. Our proposed convolutional neural network model performs competitively when compared to earlier results reported in the literature.

Related papers

• Brain Tumor Classification on MRI in Light of Molecular Markers [61.77272414423481]
  Co-deletion of the 1p/19q gene is associated with clinical outcomes in low-grade gliomas. This study aims to utilize a specially MRI-based convolutional neural network for brain cancer detection.
  arXiv  Detail & Related papers  (2024-09-29T07:04:26Z)
• 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)
• BloodCell-Net: A lightweight convolutional neural network for the classification of all microscopic blood cell images of the human body [0.0]
  Blood cell classification and counting is vital for the diagnosis of various blood-related diseases. We have proposed a DL based automated system for blood cell classification and counting from microscopic blood smear images. We classify total of nine types of blood cells, including Erythrocyte, Erythroblast, Neutrophil, Basophil, Eosinophil, Lymphocyte, Monocyte, Immature Granulocytes, and Platelet.
  arXiv  Detail & Related papers  (2024-04-01T20:38:58Z)
• Pathologist-Like Explanations Unveiled: an Explainable Deep Learning System for White Blood Cell Classification [1.516937009186805]
  HemaX is an explainable deep neural network-based model that produces pathologist-like explanations using five attributes. HemaX achieves impressive results, with an average classification accuracy of 81.08% and a Jaccard index of 89.16% for cell localization.
  arXiv  Detail & Related papers  (2023-10-20T04:59:20Z)
• Tertiary Lymphoid Structures Generation through Graph-based Diffusion [54.37503714313661]
  In this work, we leverage state-of-the-art graph-based diffusion models to generate biologically meaningful cell-graphs. We show that the adopted graph diffusion model is able to accurately learn the distribution of cells in terms of their tertiary lymphoid structures (TLS) content.
  arXiv  Detail & Related papers  (2023-10-10T14:37:17Z)
• Medulloblastoma Tumor Classification using Deep Transfer Learning with Multi-Scale EfficientNets [63.62764375279861]
  We propose an end-to-end MB tumor classification and explore transfer learning with various input sizes and matching network dimensions. Using a data set with 161 cases, we demonstrate that pre-trained EfficientNets with larger input resolutions lead to significant performance improvements.
  arXiv  Detail & Related papers  (2021-09-10T13:07:11Z)
• Wide & Deep neural network model for patch aggregation in CNN-based prostate cancer detection systems [51.19354417900591]
  Prostate cancer (PCa) is one of the leading causes of death among men, with almost 1.41 million new cases and around 375,000 deaths in 2020. To perform an automatic diagnosis, prostate tissue samples are first digitized into gigapixel-resolution whole-slide images. Small subimages called patches are extracted and predicted, obtaining a patch-level classification.
  arXiv  Detail & Related papers  (2021-05-20T18:13:58Z)
• Acute Lymphoblastic Leukemia Detection from Microscopic Images Using Weighted Ensemble of Convolutional Neural Networks [4.095759108304108]
  This article has automated the ALL detection task from microscopic cell images, employing deep Convolutional Neural Networks (CNNs) Various data augmentations and pre-processing are incorporated for achieving a better generalization of the network. Our proposed weighted ensemble model, using the kappa values of the ensemble candidates as their weights, has outputted a weighted F1-score of 88.6 %, a balanced accuracy of 86.2 %, and an AUC of 0.941 in the preliminary test set.
  arXiv  Detail & Related papers  (2021-05-09T18:58:48Z)
• Red Blood Cell Segmentation with Overlapping Cell Separation and Classification on Imbalanced Dataset [1.7219362335740878]
  Overlapping cells can cause incorrect predicted results that have to separate into multiple single RBCs before classifying. To classify multiple classes with deep learning, imbalance problems are common in medical imaging because normal samples are always higher than rare disease samples. This paper presents a new method to segment and classify red blood cells from blood smear images, specifically to tackle cell overlapping and data imbalance problems.
  arXiv  Detail & Related papers  (2020-12-02T16:49:51Z)

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.