A Novel Meta-Heuristic Optimization Algorithm in White Blood Cells燙lassification

Fathy, Khaled; Yaseen, Humam K.; Abou-Kreisha, Mohammad T.; ElDahshan, Kamal A.

doi:10.32604/cmc.2023.036322

Cited by 2 publications

(2 citation statements)

References 37 publications

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“…The extracted feature vectors are classified using the K-nearest neighbor (KNN) algorithm. Fathy et al [40] merged transfer deep learning model and support vector machine to form a hybrid model for classifying white blood cells, which was reported better than pre-trained models.…”

Section: B Deep Learning Methodsmentioning

confidence: 99%

Automatic Classification of White Blood Cells Using a Semi-Supervised Convolutional Neural Network

Song,

Wang

2024

IEEE Access

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The correct classification of white blood cell subtypes is critical in the diagnosis of blood disease. However, the performance of classical computer vision-based classification methods is heavily dependent on the features that should be carefully designed by trial and error. The machine learning-based classifier outperforms the traditional classifiers but suffers from sample labeling, which is labor intensive and time consuming. This paper presents a semi-supervised convolutional neural network that can maintain a similarly high accuracy of classification as deep learning approaches with only 10% labeled data or less. A Visual Geometry Group Net (VGGNet) model was pre-trained with a small amount of labeled data and then used to predict unlabeled data. After implementing entropy filtering and confidence filtering processes, highquality pseudo label data were obtained and served as input for the final mean teacher model training. The proposed methodology was validated on a dataset of 9069 synthetic images that correspond to five different subtypes of white blood cells. The model yielded an overall average accuracy of 94.4% with only 500 labeled samples, which is slightly lower than that of the fully supervised model with 9069 labeled samples (97.9%) but much higher than that of the fully supervised model with 500 labeled samples (86.5%). With such results, the proposed model demonstrates promising prospects for developing clinically useful solutions that are able to detect white blood cells based on blood cell images.INDEX TERMS White blood cell classification, medical imaging, deep learning, semi-supervision.

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Section: B Deep Learning Methodsmentioning

confidence: 99%

Automatic Classification of White Blood Cells Using a Semi-Supervised Convolutional Neural Network

Song,

Wang

2024

IEEE Access

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“…This data can be extremely valuable to businesses if analyzed and utilized correctly. Machine learning algorithms have proven to be highly effective in analyzing data in various domains, including business [1], medicine [2][3][4] , communication [5],intrusion detection [6], and industry [7], making them useful for data collection and analysis. With the help of these algorithms, businesses can gain more valuable insights, identify patterns, and build a deeper understanding of the collected data.…”

Section: Introductionmentioning

confidence: 99%

An Optimized Intelligent Open-Source MLaaS Framework for User-Friendly Clustering and Anomaly Detection

ElDahshan,

Abutaleb,

Elemary

et al. 2024

Preprint

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In the big data era, businesses are in dire need of intelligent solutions to rapidly extract valuable insights. However, not all companies possess the specialized expertise required to operate machine learning algorithms. To bridge this gap, this paper introduces a cost-effective, user-friendly, dependable, adaptable, and scalable solution for visualizing, analyzing, processing, and extracting valuable insights from data. The proposed solution is an optimized open-source unsupervised machine learning as a service (MLaaS) framework that caters to both experts and non-experts in machine learning. The framework aims to assist companies and organizations in solving problems related to clustering and anomaly detection, even without prior experience or internal infrastructure. With a focus on several clustering and anomaly detection techniques, the proposed framework automates data processing while allowing user intervention. Furthermore, the proposed solution is expandable; it may include additional algorithms. It is versatile and capable of handling diverse datasets by generating separate rapid artificial intelligence (AI) models for each dataset and facilitating their comparison rapidly. The proposed framework provides a solution through a Representational State Transfer (RESTful) Application Programming Interface (API), enabling seamless integration with various systems. Real-world testing of the proposed framework on customer segmentation and fraud detection data demonstrates that it is reliable, efficient, cost-effective, and time-saving. With this innovative MLaaS framework, companies may harness the full potential of business analysis.

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A Novel Meta-Heuristic Optimization Algorithm in White Blood Cells燙lassification

Cited by 2 publications

References 37 publications

Automatic Classification of White Blood Cells Using a Semi-Supervised Convolutional Neural Network

Automatic Classification of White Blood Cells Using a Semi-Supervised Convolutional Neural Network

An Optimized Intelligent Open-Source MLaaS Framework for User-Friendly Clustering and Anomaly Detection

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