An expert system for leukocyte classification using probabilistic deep feature optimization via distribution estimation

• Autorzy: Awais, Muhammad , Akram, Tallha , Alasiry, Areej , Marzougui, Mehrez , Park, Jongwoon , Chang, Byoungchol
• Języki publikacji: EN

Abstrakty

EN

White blood cells (WBCs) are essential for immune and inflammatory responses, and their precise classification is crucial for diagnosing and managing diseases. Although convolutional neural networks (CNNs) are effective for image classification, their high computational demands necessitate feature selection to enhance efficiency and interpretability. This study utilizes transfer learning with EfficientNet-B0 and DenseNet201 to extract features, along with a Bayesian-based feature selection method with a novel optimization mechanism to improve convergence. The reduced feature set is classified using soft voting across multiple classifiers. Tests on benchmark datasets achieved over 99% accuracy with fewer features, surpassing or matching existing methods.

Słowa kluczowe

PL

sieć neuronowa konwolucyjna; algorytm ewolucyjny; klasyfikacja leukocytów; uczenie maszynowe

EN

convolutional neural network; evolutionary algorithm; leukocyte classification; machine learning

• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2024
• Tom: Vol. 34, no. 4
• Strony: 579--595
• Opis fizyczny: Bibliogr. 42 poz., rys., tab., wykr.

Twórcy

autor

Awais, Muhammad

• Department of Electrical and Computer Engineering, COMSATS University Islamabad, Wah Campus, Wah Cantt, 47040, Pakistan

autor

Akram, Tallha

• Department of Information Systems, Prince Sattam bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia,

autor

Alasiry, Areej

• College of Computer Science, King Khalid University, King Abdullah Road, 61421, Abha, Saudi Arabia

autor

Marzougui, Mehrez

• College of Computer Science, King Khalid University, King Abdullah Road, 61421, Abha, Saudi Arabia

autor

Park, Jongwoon

• Department of Computer Science, Hanyang University, 222, Wangsimni-ro, Seongdong-gu, Seoul, 04763, Republic of Korea

autor

Chang, Byoungchol

• Department of Computer Science, Hanyang University, 222, Wangsimni-ro, Seongdong-gu, Seoul, 04763, Republic of Korea

Bibliografia

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Identyfikatory

DOI

10.61822/amcs-2024-0039