NDLI: Blood Slide Image Analysis to Classify WBC Types for Prediction Haematology Based on a Hybrid Model of CNN and Handcrafted Features.

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Blood Slide Image Analysis to Classify WBC Types for Prediction Haematology Based on a Hybrid Model of CNN and Handcrafted Features.

Content Provider	Europe PMC
Author	Olayah, Fekry Senan, Ebrahim Mohammed Ahmed, Ibrahim Abdulrab Awaji, Bakri
Editor	Chen, Dechang
Copyright Year	2023
Abstract	White blood cells (WBCs) are one of the main components of blood produced by the bone marrow. WBCs are part of the immune system that protects the body from infectious diseases and an increase or decrease in the amount of any type that causes a particular disease. Thus, recognizing the WBC types is essential for diagnosing the patient’s health and identifying the disease. Analyzing blood samples to determine the amount and WBC types requires experienced doctors. Artificial intelligence techniques were applied to analyze blood samples and classify their types to help doctors distinguish between types of infectious diseases due to increased or decreased WBC amounts. This study developed strategies for analyzing blood slide images to classify WBC types. The first strategy is to classify WBC types by the SVM-CNN technique. The second strategy for classifying WBC types is by SVM based on hybrid CNN features, which are called VGG19-ResNet101-SVM, ResNet101-MobileNet-SVM, and VGG19-ResNet101-MobileNet-SVM techniques. The third strategy for classifying WBC types by FFNN is based on a hybrid model of CNN and handcrafted features. With MobileNet and handcrafted features, FFNN achieved an AUC of 99.43%, accuracy of 99.80%, precision of 99.75%, specificity of 99.75%, and sensitivity of 99.68%.
Journal	Diagnostics (Basel, Switzerland)
Volume Number	13
PubMed Central reference number	PMC10252914
Issue Number	11
PubMed reference number	37296753
e-ISSN	20754418
DOI	10.3390/diagnostics13111899
Language	English
Publisher	MDPI
Publisher Date	2023-05-29
Access Restriction	Open
Rights License	Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). © 2023 by the authors.
Subject Keyword	deep learning FFNN SVM fusion features handcrafted features WBC haematology
Content Type	Text
Resource Type	Article
Subject	Clinical Biochemistry

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