Computer aided detection and classification of acute lymphoblastic leukemia cell subtypes based on microscopic image analysis

2016 ◽  
Vol 79 (10) ◽  
pp. 908-916 ◽  
Author(s):  
Morteza MoradiAmin ◽  
Ahmad Memari ◽  
Nasser Samadzadehaghdam ◽  
Saeed Kermani ◽  
Ardeshir Talebi
Keyword(s):  
Image Analysis ◽  
Acute Lymphoblastic Leukemia ◽  
Lymphoblastic Leukemia ◽  
Leukemia Cell ◽  
Microscopic Image ◽  
Computer Aided Detection ◽  
Microscopic Image Analysis ◽  
Computer Aided

scholarly journals A YOLO and convolutional neural network for the detection and classification of leukocytes in leukemia

2022 ◽  
Vol 25 (1) ◽  
pp. 200
Author(s):  
Shakir Mahmood Abas ◽  
Adnan Mohsin Abdulazeez ◽  
Diyar Qader Zeebaree
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Lymphoblastic Leukemia ◽  
White Blood Cells ◽  
Complete Information ◽  
Computer Aided Detection ◽  
Hand Segmentation ◽  
Computer Aided

The developing of deep learning systems that used for chronic diseases diagnosing is challenge. Furthermore, the localization and identification of objects like white blood cells (WBCs) in leukemia without preprocessing or traditional hand segmentation of cells is a challenging matter due to irregular and distorted of nucleus. This paper proposed a system for computer-aided detection depend completely on deep learning with three models computer-aided detection (CAD3) to detect and classify three types of WBC which is fundamentals of leukemia diagnosing. The system used modified you only look once (YOLO v2) algorithm and convolutional neural network (CNN). The proposed system trained and evaluated on dataset created and prepared specially for the addressed problem without any traditional segmentation or preprocessing on microscopic images. The study proved that dividing of addressed problem into sub-problems will achieve better performance and accuracy. Furthermore, the results show that the CAD3 achieved an average precision (AP) up to 96% in the detection of leukocytes and accuracy 94.3% in leukocytes classification. Moreover, the CAD3 gives report contain a complete information of WBC. Finally, the CAD3 proved its efficiency on the other dataset such as acute lymphoblastic leukemia image database (ALL-IBD1) and blood cell count dataset (BCCD).

scholarly journals Acute lymphoblastic leukemia cells image analysis with deep bagging ensemble learning

2019 ◽  
Author(s):  
Ying Liu ◽  
Feixiao Long
Keyword(s):  
Image Analysis ◽  
Acute Lymphoblastic Leukemia ◽  
Ensemble Learning ◽  
Lymphoblastic Leukemia ◽  
Preliminary Test ◽  
Blood Cancer ◽  
Analysis Task ◽  
Training Samples ◽  
Bagging Ensemble

AbstractAcute lymphoblastic leukemia (ALL) is a blood cancer which leads 111,000 depth globally in 2015. Recently, diagnosing ALL often involves the microscopic image analysis with the help of deep learning (DL) techniques. However, as most medical related problems, deficiency training samples and minor visual difference between ALL and normal cells make the image analysis task quite challenging. Herein, an augmented image enhanced bagging ensemble learning with elaborately designed training subsets were proposed to tackle above challenges. The weightedF1-scores of preliminary test set and final test are 0.84 and 0.88 respectively employing our ensemble model predictions and ranked within top 10% in ISBI-2019 Classification of Normal vs. Malignant White Blood Cancer Cells contest. Our results preliminarily show the efficacy and accuracy of employing DL based techniques in ALL cells image analysis.

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