An adaptive leukocyte nucleus segmentation using genetic algorithm

Huang, Der-Chen; Hung, Kun-Ding; Chan, Yung-Kuan

doi:10.1109/ispacs.2012.6473552

Cited by 2 publications

(2 citation statements)

References 8 publications

(13 reference statements)

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“…In [ 69 ], PCA (Principal Component Analysis) technique is used to reduce the features to avoid any redundancy. Genetic Algorithm is also used to select important features [ 70 , 71 ]. PPCA (Probabilistic Principal Component Analysis) technique also gives better performance for features reduction [ 13 ].…”

Section: Methodsmentioning

confidence: 99%

Computer-Aided Diagnosis of Acute Lymphoblastic Leukaemia

Shafique

Tehsin

2018

Computational and Mathematical Methods in Medicine

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Leukaemia is a form of blood cancer which affects the white blood cells and damages the bone marrow. Usually complete blood count (CBC) and bone marrow aspiration are used to diagnose the acute lymphoblastic leukaemia. It can be a fatal disease if not diagnosed at the earlier stage. In practice, manual microscopic evaluation of stained sample slide is used for diagnosis of leukaemia. But manual diagnostic methods are time-consuming, less accurate, and prone to errors due to various human factors like stress, fatigue, and so forth. Therefore, different automated systems have been proposed to wrestle the glitches in the manual diagnostic methods. In recent past, some computer-aided leukaemia diagnosis methods are presented. These automated systems are fast, reliable, and accurate as compared to manual diagnosis methods. This paper presents review of computer-aided diagnosis systems regarding their methodologies that include enhancement, segmentation, feature extraction, classification, and accuracy.

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Section: Methodsmentioning

confidence: 99%

Computer-Aided Diagnosis of Acute Lymphoblastic Leukaemia

Shafique

Tehsin

2018

Computational and Mathematical Methods in Medicine

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“…Citation information: DOI 10.1109/ACCESS.2021.3114059, IEEE Access Author et al: Preparation of Papers for IEEE TRANSACTIONS and JOURNALSare also efficient methods for feature reduction[106],[122].Classification of leukemia is normally through supervised learning where model is trained on the labelled data and tested on the new data that is different from the training data[85]. Classification model for leukemia can be partially or fully automated.…”

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confidence: 99%

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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An adaptive leukocyte nucleus segmentation using genetic algorithm

Cited by 2 publications

References 8 publications

Computer-Aided Diagnosis of Acute Lymphoblastic Leukaemia

Computer-Aided Diagnosis of Acute Lymphoblastic Leukaemia

Automated Diagnosis of Leukemia: A Comprehensive Review

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