Detection and Classification of Leukocytes in Leukemia using YOLOv2 with CNN
Shakir M. Abas, Adnan M. Abdulazeez
Asian Journal of Research in Computer Science · pp. 64–75 · Published 12 May 2021
10.9734/ajrcos/2021/v8i330204Abstract
The development of machine learning systems that used for diagnosis of chronic diseases is challenging mainly due to lack of data and difficulty of diagnosing. This paper compared between two proposed systems for computer-aided diagnosis (CAD) to detect and classify three types of white blood cells which are fundamental of an acute leukemia diagnosis. Both systems depend on the You Only Look Once (YOLOv2) algorithm based on Convolutional Neural Network (CNN). The first system detects and classifies leukocytes at the same time called computer-aided diagnosis with one model (CADM1). The second system separates detection and classification by using two models called computer-aided diagnosis with two models (CADM2). The main purpose of the paper is proving the high performance and accuracy by fragmentation of the main task into sub-tasks through comparing between CADM1 and CADM2. Also, the paper proved that can be depending only on deep learning without any traditional segmentation and preprocessing on the microscopic image. The (CADM1) achieved average precision for detection and classification class1=56%, class2=69% and class3 72% while (CADM2) achieved average precision up to 94% for detect leukocytes and accuracy 92.4% for classification. The result of the second system is very suitable for diagnosis leukocytes in leukemia.
References (28)
- 1 SeaShips: A Large-Scale Precisely Annotated Dataset for Ship Detection [DOI]
- 2 Acute Lymphoblastic Leukemia Detection and Classification of Its Subtypes Using Pretrained Deep Convolutional Neural Networks [DOI]
- 3 A Comparative Analysis of Gradient Descent-Based Optimization Algorithms on Convolutional Neural Networks [DOI]
- 4 Classification of acute lymphoblastic leukemia using deep learning [DOI]
- 5 A deep convolutional neural network for classification of red blood cells in sickle cell anemia [DOI]
- 6 Applying Faster R-CNN for Object Detection on Malaria Images [DOI]
- 7 Convolutional neural networks for computer-aided detection or diagnosis in medical image analysis: An overview [DOI]
- 8 Deep learning approach to peripheral leukocyte recognition [DOI]
- 9 Improved Threshold Based and Trainable Fully Automated Segmentation for Breast Cancer Boundary and Pectoral Muscle in Mammogram Images [DOI]
- 10 Leukemia Blood Cell Image Classification Using Convolutional Neural Network [DOI]
- 11 Gene Selection and Classification of Microarray Data Using Convolutional Neural Network [DOI]
- 12 Computer Aided Diagnostic System for Detection of Leukemia Using Microscopic Images [DOI]
- 13 Machine learning and Region Growing for Breast Cancer Segmentation [DOI]
- 14 Convolutional Neural Networks for Recognition of Lymphoblast Cell Images [DOI]
- 15 Trainable Model Based on New Uniform LBP Feature to Identify the Risk of the Breast Cancer [DOI]
- 16 Gene Expression Classification Based on Deep Learning [DOI]
- 17 Red blood cell segmentation by thresholding and Canny detector [DOI]
- 18 Subclass Separation of White Blood Cell Images Using Convolutional Neural Network Models [DOI]
- 19 Management of Wireless Communication Systems Using Artificial Intelligence-Based Software Defined Radio [DOI]
- 20 Leukocyte recognition with convolutional neural network [DOI]
- 21 Guide to build YOLO, a creativity-stimulating robot for children [DOI]
- 22 Fused faster RCNNs for efficient detection of the license plates [DOI]
- 23 custom: Medical Text Classification Based on Convolutional Neural Network: A Review
- 24 Gene clustering with partition around mediods algorithm based on weighted and normalized mahalanobis distance [DOI]
- 25 Convolutional Neural Network for Diagnosing Skin Cancer [DOI]
- 26 Acute Leukemia Classification Using Convolution Neural Network in Clinical Decision Support System [DOI]
- 27 Segmentation of nucleus and cytoplasm of white blood cells using Gram-Schmidt orthogonalization and deformable models [DOI]
- 28 YOLO-LITE: A Real-Time Object Detection Algorithm Optimized for Non-GPU Computers [DOI]
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