Leucocyte classification for leukaemia detection using image processing techniques

Putzu, Lorenzo; Caocci, Giovanni; Ruberto, Cecilia Di

doi:10.1016/j.artmed.2014.09.002

Cited by 246 publications

(131 citation statements)

References 12 publications

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“…Roundness bernilai satu untuk objek yang berbentuk bundar, sedangkan bernilai kurang dari satu untuk objek yang menjauhi bentuk bundar. Pada penelitian [14] roundness threshold yang diusulkan sebesar 0.8.…”

Section: ) Identifikasi Touching Cellunclassified

“…Nilai ambang solidity yang digunakan pada penelitian ini yaitu 0.9 [14]. Semua objek bernilai solidity di bawah nilai ambang (threshold) akan dihapus.…”

Section: = (8)unclassified

“…Beberapa penelitian menggunakan citra apus darah normal [8], [9], [10], [5], [11] dan juga citra apus darah tepi leukemia [12], [13], [14].…”

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“…Permasalahan lain yang berpengaruh dalam proses analisis sel darah putih adalah keberadaan sel yang bersentuhan (touching cell) sehingga sel-sel tersebut perlu dideteksi dan dipisahkan [14]. Proses identifikasi dan klasifikasi sel darah putih memerlukan informasi dari tiap sel (single cell) dan untuk mendapatkan informasi tersebut sel yang bersentuhan harus dipisahkan dengan baik agar dapat meningkatkan akurasi pada tahap tersebut.…”

unclassified

“…Pada sel darah putih, penelitian yang menggunakan watershed untuk memisahkan sel yang bersentuhan diusulkan pada penelitian [14]. Citra sel darah putih diidentifikasi untuk penentuan sel yang bersentuhan atau tidak menggunakan analisis pada kebulatan (roundness) sel darah putih.…”

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Segmentasi Dan Pemisahan Sel Darah Putih Bersentuhan Menggunakan K-Means Dan Hierarchical Clustering Analysis Pada Citra Leukemia Myeloid Akut

Harto

Fatichah

2017

JUTI

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Section: ) Identifikasi Touching Cellunclassified

“…Nilai ambang solidity yang digunakan pada penelitian ini yaitu 0.9 [14]. Semua objek bernilai solidity di bawah nilai ambang (threshold) akan dihapus.…”

Section: = (8)unclassified

“…Beberapa penelitian menggunakan citra apus darah normal [8], [9], [10], [5], [11] dan juga citra apus darah tepi leukemia [12], [13], [14].…”

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Segmentasi Dan Pemisahan Sel Darah Putih Bersentuhan Menggunakan K-Means Dan Hierarchical Clustering Analysis Pada Citra Leukemia Myeloid Akut

Harto

Fatichah

2017

JUTI

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Identification of white blood cells for the diagnosis of acute myeloid leukemia

Roy

Ameer

2022

Int J Imaging Syst Tech

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The diagnosis of hematologic malignancies such as leukemia includes identifying malignant white blood cells. Manual microscopic analysis of white blood cells is time-consuming and involves the assistance of medical specialists, and its accuracy may be affected by their abilities. Our objective is to develop an automated system that can identify 15 categories of white blood cells to assist medical practitioners in diagnosing acute myeloid leukemia. The proposed approach uses an ensemble model built with transfer learning-based pretrained networks to categorize 15 kinds of white blood cells. An over-sampling strategy is used to alleviate the problem of class imbalance, followed by data augmentation. For experimentation, a microscopic blood image dataset containing 18 365 cells images from 200 individuals representing 15 different forms of leukocytes is used. For cells with more than 400 images available, the suggested technique achieves an F1 score of more than 91%. Myeloblasts, which are frequent in myeloid leukemias and are detected in the peripheral blood, are recognized with an average precision of 95.74%. This work describes an image processing strategy that employs ensemble learning to assist in diagnosing acute myeloid leukemia by classifying 15 different types of leukocytes.Experiments show that our technique is both practical and effective. Extensive research supports the use of the leukocyte classifier in real-world medical applications, such as assisting clinicians in diagnosing diseases and decreasing human resource requirements.

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An efficient lymphocytic leukemia detection based on EfficientNets and ensemble voting classifier

Baby

Juliet

Raj³

2022

Int J Imaging Syst Tech

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Early detection of leukemia increases the chances of a speedier recovery. If a patient exhibits any symptoms, doctors would often examine a blood sample slide under a microscope to detect hematological malignancies. Manually categorizing leukocytes as normal or abnormal requires examining the many characteristics of the cells, which is time-consuming and error-prone. This research aims to create a transfer learning-based Acute Lymphocytic Leukemia (ALL) detection system that is both efficient and easy. To overcome the critical challenges associated with feature extraction, we used EfficientNet, the most recent and most substantial deep learning model. In this article, eight EfficientNets variations are used to extract features and are compared based on classification accuracy. This work uses an ensemble of three sophisticated classifiers, namely Support Vector Machine (SVM), Random Forest, and Logistic Regression, which achieves a classification accuracy of 98.5%.

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Leucocyte classification for leukaemia detection using image processing techniques

Cited by 246 publications

References 12 publications

Segmentasi Dan Pemisahan Sel Darah Putih Bersentuhan Menggunakan K-Means Dan Hierarchical Clustering Analysis Pada Citra Leukemia Myeloid Akut

Segmentasi Dan Pemisahan Sel Darah Putih Bersentuhan Menggunakan K-Means Dan Hierarchical Clustering Analysis Pada Citra Leukemia Myeloid Akut

Identification of white blood cells for the diagnosis of acute myeloid leukemia

An efficient lymphocytic leukemia detection based on EfficientNets and ensemble voting classifier

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