Multi Features Based Approach for White Blood Cells Segmentation and Classification in Peripheral Blood and Bone Marrow Images

Benomar, Mohammed Lamine; Chikh, Amine; Descombes, Xavier; Benazzouz, Mourtada

doi:10.1504/ijbet.2019.10030162

Cited by 5 publications

(1 citation statement)

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“…The detection method using traditional machine learning produces high accuracy. However, it requires selecting of a reliable feature extraction method (Falcón-ruiz et al, 2010) (Mourtada et al, 2019). Another issue is the need for big data or analysis on a large number of datasets in a relatively short time (Al-Dulaimi et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Automatic Leukocytes Classification using Deep Convolutional Neural Network

Hadiyoso

Aulia

2023

ELKOMIKA

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Sel darah putih atau leukosit adalah salah satu bagian darah yang bertanggung jawab untuk sistem kekebalan tubuh. Penghitungan setiap jenis leukosit merupakan hal yang krusial untuk menentukan status kesehatan. Sel darah dihitung menggunakan hematology analyzer. Namun, perangkat ini hanya tersedia di laboratorium klinik pusat atau rumah sakit. Saat ini masih banyak clinician yang melakukan perhitungan manual dengan memperkirakan jumlah leukosit menggunakan mikroskop. Hal ini berpotensi menimbulkan kesalahan perhitungan yang tinggi. Oleh karena itu, penelitian ini mengusulkan suatu sistem yang dapat mengklasifikasikan jenis-jenis leukosit. Metode convolutional neural network (CNN) dengan arsitektur VGG-19 digunakan dalam klasifikasi leukosit. Beberapa skenario pengujian dengan mengubah parameter epoch dan ukuran batch diterapkan untuk mendapatkan akurasi terbaik. Hasil simulasi model pembelajaran yang digunakan dapat menghasilkan akurasi hingga 100% untuk mengklasifikasikan neutrofil, eosinofil, monosit, dan limfosit. Hasil ini dicapai dengan menggunakan pengoptimal Adam, Epoch=5 dan batch size=60.

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Section: Introductionmentioning

confidence: 99%

Automatic Leukocytes Classification using Deep Convolutional Neural Network

Hadiyoso

Aulia

2023

ELKOMIKA

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Automated recognition of white blood cells using deep learning

et al. 2020

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Automated Diagnosis of Leukemia: A Comprehensive Review

Shah

Naqvi

Naveed³

et al. 2021

IEEE Access

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Leukemia is the rapid production of abnormal white blood cells that consequently affects the blood and damages the bone marrow. The overproduction of abnormal and immature white blood cells leads to the damage of the immune system due to the reduced production of red blood cells and platelets by the bone marrow of the body. This hematological malignancy is generally diagnosed by manual methods such as complete blood count (CBC), bone marrow aspiration, or microscopic examination of the blood smear. Nevertheless, the manual methods of leukemia diagnosis are economical but are found to be less reliable, time-consuming, and hectic. Technological advancement in the medical field has effectively addressed these issues in the past. The problems in the manual diagnosis of leukemia detection have been overcome by the development of automated methods using the computer-aided diagnostic (CAD) systems for efficient and reliable leukemia diagnosis. Since the last decade, multiple approaches have been proposed for the CAD systems regarding pre-processing, segmentation, feature extraction, feature selection, and for the improvement of the classification accuracy of the CAD system for the leukemia detection. This paper presents a comprehensive review of the CAD systems for the detection of the various types of leukemia. The review presented here entails the details of various CAD systems for the automated diagnosis of various types of leukemia and analyses their methodologies in terms of their efficiency in pre-processing, segmentation, feature extraction and selection, and overall classification accuracy of the CAD system.INDEX TERMS Acute lymphoblastic leukemia (ALL), acute myeloid leukemia (AML), chronic lymphocytic leukemia (CLL), chronic myeloid leukemia (CML), computer-aided diagnostic (CAD), Leukemia.

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Multi Features Based Approach for White Blood Cells Segmentation and Classification in Peripheral Blood and Bone Marrow Images

Cited by 5 publications

References 0 publications

Automatic Leukocytes Classification using Deep Convolutional Neural Network

Automatic Leukocytes Classification using Deep Convolutional Neural Network

Automated recognition of white blood cells using deep learning

Automated Diagnosis of Leukemia: A Comprehensive Review

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