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

Abstract

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.

COVID-19 image classification using deep features and fractional-order marine predators algorithm

“…In this paper, after applying Chi-square, the feature vector is minimized for both datasets from 51200 to 2000. Tree based classifier are the most popular method to calculate feature importance to improve the classification since they have high accuracy, robustness, and simple 38 . For each decision tree, node importance is calculated using Gini importance, Eq.…”

“…Then the best solutions are reached which determine the optimal/relevant features that should be used to address the desired output via several performance measures. Inspired by our recent work 38 , where VGG-19 besides statistically enhanced Salp Swarm Algorithm was applied to select the best features for White Blood Cell Leukaemia classification. Also, other recent published works 39 , who combined a CNN architecture with Weighted Symmetric Uncertainty (WSU) to select optimal features for traffic classification.…”

Wir können über uns nachdenken – der Computer nicht

“…Das bildet -stark vereinfacht gesprochen -eine Abstraktion der vielen Inputs auf allgemeinere Begriffe nach. Diese Netzwerke sind komplexer zu trainieren ("back-propagation" und andere Schritte), schaffen aber, oft mit weiteren Strategien aus der künstlichen Intelligenzforschung weiter verbessert, auch Erstaunliches, etwa optische Bilderkennung von Leukämiezellen durch verbesserte Schwarmoptimierung (Sahlol et al 2020) oder die automatische Erkennung der Sekundärstruktur und von Oligonukleotiden in elektronenmikroskopischen Aufnahmen (Mostosi et al 2020), sodass mit diesem Deep learning Ansatz schließlich sogar Antibiotika entdeckt werden können (Stokes et al 2020) oder die Energiepotentiale und damit auch die dreidimensionale Struktur von Proteinen (Senior et al 2020).…”

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“…Accuracy and reduced computational complexity were achieved according to the obtained results. [7]. Ramesh et al proposed a classify framework based on color information and morphology.…”