Efficient Classification of White Blood Cell Leukemia with Improved Swarm Optimization of Deep Features

White Blood Cell (WBC) Leukaemia is caused by excessive production of leukocytes in the bone marrow, and image-based detection of malignant WBCs is important for its detection. Convolutional Neural Networks (CNNs) present the current state-of-the-art for this type of image classification, but their...

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Bibliographic Details
Published in:Scientific reports 2020-02, Vol.10 (1), p.2536-2536, Article 2536
Main Authors: Sahlol, Ahmed T., Kollmannsberger, Philip, Ewees, Ahmed A.
Format: Article
Language:English
Subjects:
14/63
631/114/1564
639/705/117
692/4028/67/1990/283/2125
Algorithms
Bone marrow
Bone Marrow - diagnostic imaging
Bone Marrow - pathology
Classification
Computer applications
Humanities and Social Sciences
Humans
Image Processing, Computer-Assisted
Leukemia
Leukemia - blood
Leukemia - diagnostic imaging
Leukemia - pathology
Leukocytes
Leukocytes - pathology
Machine Learning
multidisciplinary
Neural networks
Neural Networks, Computer
Optimization
Science
Science (multidisciplinary)
Statistical analysis
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Summary:White Blood Cell (WBC) Leukaemia is caused by excessive production of leukocytes in the bone marrow, and image-based detection of malignant WBCs is important for its detection. Convolutional Neural Networks (CNNs) present the current state-of-the-art for this type of image classification, but their computational cost for training and deployment can be high. We here present an improved hybrid approach for efficient classification of WBC Leukemia. We first extract features from WBC images using VGGNet, a powerful CNN architecture, pre-trained on ImageNet. The extracted features are then filtered using a statistically enhanced Salp Swarm Algorithm (SESSA). This bio-inspired optimization algorithm selects the most relevant features and removes highly correlated and noisy features. We applied the proposed approach to two public WBC Leukemia reference datasets and achieve both high accuracy and reduced computational complexity. The SESSA optimization selected only 1 K out of 25 K features extracted with VGGNet, while improving accuracy at the same time. The results are among the best achieved on these datasets and outperform several convolutional network models. We expect that the combination of CNN feature extraction and SESSA feature optimization could be useful for many other image classification tasks.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-020-59215-9