A Novel Deep Learning based Model for Erythrocytes Classification and Quantification in Sickle Cell Disease

• URL: http://arxiv.org/abs/2305.01663v1
• Date: Tue, 2 May 2023 10:28:07 GMT
• Title: A Novel Deep Learning based Model for Erythrocytes Classification and Quantification in Sickle Cell Disease
• Authors: Manish Bhatia, Balram Meena, Vipin Kumar Rathi, Prayag Tiwari, Amit Kumar Jaiswal, Shagaf M Ansari, Ajay Kumar, Pekka Marttinen
• Abstract summary: We proposed a customized deep convolutional neural network (CNN) model to classify and quantify the distorted and normal morphology of erythrocytes. We focused on three well-defined erythrocyte shapes, including discocytes, oval, and sickle. We used 18 layered deep CNN architecture to identify and quantify these shapes with 81% accuracy, outperforming other models.
• Score: 16.351002089332994
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The shape of erythrocytes or red blood cells is altered in several pathological conditions. Therefore, identifying and quantifying different erythrocyte shapes can help diagnose various diseases and assist in designing a treatment strategy. Machine Learning (ML) can be efficiently used to identify and quantify distorted erythrocyte morphologies. In this paper, we proposed a customized deep convolutional neural network (CNN) model to classify and quantify the distorted and normal morphology of erythrocytes from the images taken from the blood samples of patients suffering from Sickle cell disease ( SCD). We chose SCD as a model disease condition due to the presence of diverse erythrocyte morphologies in the blood samples of SCD patients. For the analysis, we used 428 raw microscopic images of SCD blood samples and generated the dataset consisting of 10, 377 single-cell images. We focused on three well-defined erythrocyte shapes, including discocytes, oval, and sickle. We used 18 layered deep CNN architecture to identify and quantify these shapes with 81% accuracy, outperforming other models. We also used SHAP and LIME for further interpretability. The proposed model can be helpful for the quick and accurate analysis of SCD blood samples by the clinicians and help them make the right decision for better management of SCD.

Related papers

• Deep Learning Algorithms for Early Diagnosis of Acute Lymphoblastic Leukemia [0.0]
  Acute lymphoblastic leukemia (ALL) is a form of blood cancer that affects the white blood cells. In this study, we propose a binary image classification model to assist in the diagnostic process of ALL.
  arXiv  Detail & Related papers  (2024-07-14T15:35:39Z)
• 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)
• Machine learning based lens-free imaging technique for field-portable cytometry [0.0]
  The performance of our proposed method shows an increase in accuracy >98% along with the signal enhancement of >5 dB for most of the cell types. The model is adaptive to learn new type of samples within a few learning iterations and able to successfully classify the newly introduced sample.
  arXiv  Detail & Related papers  (2022-03-02T07:09:29Z)
• Pathological Analysis of Blood Cells Using Deep Learning Techniques [0.0]
  A neural based network has been proposed for classification of blood cells images into various categories. The performance of proposed model is better than existing standard architectures and work done by various researchers.
  arXiv  Detail & Related papers  (2021-11-05T05:37:10Z)
• Interpretable Automated Diagnosis of Retinal Disease using Deep OCT Analysis [7.005458308454871]
  We develop a CNN-based model for accurate classification of OCT scans. We place an emphasis on producing both qualitative and quantitative explanations of the model's decisions. Our work is the first to produce detailed explanations of the model's decisions.
  arXiv  Detail & Related papers  (2021-09-03T17:59:34Z)
• 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)
• Personalized pathology test for Cardio-vascular disease: Approximate Bayesian computation with discriminative summary statistics learning [48.7576911714538]
  We propose a platelet deposition model and an inferential scheme to estimate the biologically meaningful parameters using approximate computation. This work opens up an unprecedented opportunity of personalized pathology test for CVD detection and medical treatment.
  arXiv  Detail & Related papers  (2020-10-13T15:20:21Z)
• M3Lung-Sys: A Deep Learning System for Multi-Class Lung Pneumonia Screening from CT Imaging [85.00066186644466]
  We propose a Multi-task Multi-slice Deep Learning System (M3Lung-Sys) for multi-class lung pneumonia screening from CT imaging. In addition to distinguish COVID-19 from Healthy, H1N1, and CAP cases, our M 3 Lung-Sys also be able to locate the areas of relevant lesions.
  arXiv  Detail & Related papers  (2020-10-07T06:22:24Z)
• Select-ProtoNet: Learning to Select for Few-Shot Disease Subtype Prediction [55.94378672172967]
  We focus on few-shot disease subtype prediction problem, identifying subgroups of similar patients. We introduce meta learning techniques to develop a new model, which can extract the common experience or knowledge from interrelated clinical tasks. Our new model is built upon a carefully designed meta-learner, called Prototypical Network, that is a simple yet effective meta learning machine for few-shot image classification.
  arXiv  Detail & Related papers  (2020-09-02T02:50:30Z)
• Co-Heterogeneous and Adaptive Segmentation from Multi-Source and Multi-Phase CT Imaging Data: A Study on Pathological Liver and Lesion Segmentation [48.504790189796836]
  We present a novel segmentation strategy, co-heterogenous and adaptive segmentation (CHASe) We propose a versatile framework that fuses appearance based semi-supervision, mask based adversarial domain adaptation, and pseudo-labeling. CHASe can further improve pathological liver mask Dice-Sorensen coefficients by ranges of $4.2% sim 9.4%$.
  arXiv  Detail & Related papers  (2020-05-27T06:58:39Z)

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.