Recognition of Different Types of Leukocytes Using YOLOv2 and Optimized Bag-of-Features

Sharif, Muhammad; Amin, Javeria; Siddiqa, Ayesha; Khan, Habib Ullah; Malik, Muhammad Sheraz Arshad; Anjum, Muhammad Almas; Kadry, Seifedine

doi:10.1109/access.2020.3021660

Cited by 48 publications

(23 citation statements)

References 55 publications

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“…The authors in Shafique and Tehsin [4] developed a deep CNN (DCNN) model, trained on ALL-IDB dataset augmented with 50 private images, for the classification of ALL and its subtypes using pre-trained AlexNet and fine-tuning. Sharif et al [11] recommended a YOLOv2-Nucleus-cytoplasm based scheme for WBCs localization using blood smear images. In that article, the Bag-of-Features were extracted from blood smear images of WBCs and optimized by using Particle Swarm Optimization for the classification task.…”

Section: Literature Surveymentioning

confidence: 99%

Classification of Acute Lymphoblastic Leukemia through the Fusion of Local Descriptors

Wady¹

2022

UHD J SCI TECH

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Leukemia is characterized by an abnormal proliferation of leukocytes in the bone marrow and blood, which is usually detected by pathologists using a microscope to examine a blood smear. Leukemia identification and diagnosis in advance are a trending topic in medical applications for decreasing the death toll of individuals with Acute Lymphoblastic Leukemia (ALL). It is critical to analyze the white blood cells for the identification of ALL for which the blood smear images are utilized. This paper discusses and presents a micro-pattern descriptor, called Local Directional Number Pattern along with Multi-scale Weber Local Descriptor for feature extraction task to determine cancerous and noncancerous blood cells. A balanced dataset with 260 blood smear images from the ALL-IDB2 dataset was used as training data. Consequently, a proposed model was constructed by applying different individual and combined feature extraction methods, and fed into the machine learning classifiers (Decision Tree, Ensemble, K-Nearest Neighbors, Naïve Bayes, and Random Forest) to determine cancerous and noncancerous blood cells. Experimental results indicate that the developed feature fusion technique assured a reasonable performance compared to other existing works with a testing average accuracy of 97.69 ± 1.83% using Ensemble classifier.

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Section: Literature Surveymentioning

confidence: 99%

Classification of Acute Lymphoblastic Leukemia through the Fusion of Local Descriptors

Wady¹

2022

UHD J SCI TECH

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“…Localization error means the bounding box that does not fit the size of the detected object. Accordingly, YOLOv2 is aimed at improving accuracy of classification (mAP) and keeping an object detection speed [13]. To improve accuracy of the YOLOv2 object detection, the following method is used:…”

Section: B Extraction Of Video Object Using Yolo9000mentioning

confidence: 99%

Associative Knowledge Graph Using Fuzzy Clustering and Min-Max Normalization in Video Contents

2021

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Video content data have a variety of objects that could be associated with each other. Although content data contains similar objects or themes, their associations can become ambiguous. Accordingly, if associations between video content data are found in general association rules, their accuracy and confidence are low. Therefore, this study proposes the associative knowledge graph using fuzzy clustering and min-max normalization in video contents. With the use of the objects of video content, the proposed method finds clear and accurate associations between video content data and generates a knowledge graph. In the first step, the streaming video content data massively generated are collected, and objects in each image video are classified by an object detection algorithm. In the second step, normalization is executed in consideration of the different length of each video content and generate transaction data based on object frequency. In this way, it is possible to consider all the collected video content in the same condition. Additionally, it is possible to find unnecessary objects and significant objects in video content. Lastly, the degree of ambiguity is analyzed through fuzzy clustering using a probability that each object is involved in a group. Associations between fuzzy-clustered objects are extracted and an association knowledge graph is created. In this way, the accuracy and confidence of associations between different video content data are improved. As for performance, the association knowledge graph generated in the proposed method was better than a conventional association rule method in terms of the count of generated rules, support, and confidence.

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“…In [5], a modified YOLOv1-based neural network was proposed for object detection. In [6][7][8], the YOLOv2 lgorithm was widely used in target detection. In [9], An automatic image recognition diagnosis system was established using a YOLOv2 neural network through deep learning, the performance of the system in thyroid nodule diagnosis was evaluated, and the value of artificial intelligence in clinical practice was discussed.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Fire Detection Method for Electric Vehicle Charging Stations Based on Machine Vision

Zhang

Yang

Gao

et al. 2022

WEVJ

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During the charging process of electric vehicles (EV), the circuit inside the charger plug is connected in series, the charger input voltage does not match the rated input voltage, the temperature caused by the severe heating of the charging time is too high for too long, and other factors are very likely to trigger a fire in the vehicle charging pile. In this paper, an improved You Only Look Once v4 (YOLOv4) real-time target detection algorithm based on machine vision is proposed to monitor the site based on existing monitoring equipment, transmit live video information in real-time, expand the monitoring range, and significantly reduce the cost of use. During the experiment, the improved neural network model was trained by a homemade fire video image dataset, and a K-means clustering algorithm iwasintroduced to recalculate the anchor frame size for the specific object of flame; the existing dataset was used to perform multiple divisions by using a tenfold cross-validation algorithm, thus avoiding the selection of chance hyperparameters and models that do not have generalization ability because of special divisions. The experimental results show that the improved algorithm is fast and accurate in detecting large-size flames in real-time and small-size flames at the beginning of a fire, with a detection speed of 43 fps/s, mAP value of 91.53%, and F1 value of 0.91. Compared with YOLOv3 and YOLOv4 models, the improved model is sensitive to detecting different sizes of flames. It can suppress false alarms well in a variety of complex lighting environments. The prediction frame size fits the area where the target is located, the detection accuracy remains stable, and the comprehensive performance of the network model is significantly improved to meet the demand of real-time monitoring. It is significant for developing the EV industry and enhancing emergency response capability.

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Recognition of Different Types of Leukocytes Using YOLOv2 and Optimized Bag-of-Features

Cited by 48 publications

References 55 publications

Classification of Acute Lymphoblastic Leukemia through the Fusion of Local Descriptors

Classification of Acute Lymphoblastic Leukemia through the Fusion of Local Descriptors

Associative Knowledge Graph Using Fuzzy Clustering and Min-Max Normalization in Video Contents

Real-Time Fire Detection Method for Electric Vehicle Charging Stations Based on Machine Vision

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