RETRACTED ARTICLE: Peripheral Blood Smear Images Classification for Acute Lymphoblastic Leukemia Diagnosis with an Improved Convolutional Neural Network

Özbay, Erdal; Özbay, Feyza Altunbey; Gharehchopogh, Farhad Soleimanian

doi:10.1007/s42235-023-00441-y

Cited by 26 publications

(1 citation statement)

References 59 publications

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“…Özbay et al proposed an improved ResNet50 convolutional neural network model for detecting acute lymphoblastic leukemia and its sub-types, which is hybridized through particle swarm optimization. Research shows that this method can help laboratory workers distinguish different types of acute lymphoblastic leukemia, and determine corresponding diagnostic and treatment plans [ 18 ]. S F Gharehchopogh et al proposed an improved asymmetric self-organizing mapping asymmetric clustering method.…”

Section: Related Workmentioning

confidence: 99%

Feature selection algorithm based on optimized genetic algorithm and the application in high-dimensional data processing

Feng

2024

PLoS ONE

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High-dimensional data is widely used in many fields, but selecting key features from it is challenging. Feature selection can reduce data dimensionality and weaken noise interference, thereby improving model efficiency and enhancing model interpretability. In order to improve the efficiency and accuracy of high-dimensional data processing, a feature selection method based on optimized genetic algorithm is proposed in this study. The algorithm simulates the process of natural selection, searches for possible subsets of feature, and finds the subsets of feature that optimizes the performance of the model. The results show that when the value of K is less than 4 or more than 8, the recognition rate is very low. After adaptive bias filtering, 724 features are filtered to 372, and the accuracy is improved from 0.9352 to 0.9815. From 714 features to 406 Gaussian codes, the accuracy is improved from 0.9625 to 0.9754. Among all tests, the colon has the highest average accuracy, followed by small round blue cell tumor(SRBCT), lymphoma, central nervous system(CNS) and ovaries. The green curve is the best, with stable performance and a time range of 0–300. While maintaining the efficiency, it can reach 4.48 as soon as possible. The feature selection method has practical significance for high-dimensional data processing, improves the efficiency and accuracy of data processing, and provides an effective new method for high-dimensional data processing.

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Section: Related Workmentioning

confidence: 99%

Feature selection algorithm based on optimized genetic algorithm and the application in high-dimensional data processing

Feng

2024

PLoS ONE

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A Hybrid Multi-population Optimization Algorithm for Global Optimization and Its Application on Stock Market Prediction

Alizadeh,

Gharehchopogh,

Masdari

et al. 2024

Comput Econ

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Unraveling Brain Synchronisation Dynamics by Explainable Neural Networks using EEG Signals: Application to Dyslexia Diagnosis

Gallego-Molina,

Ortiz,

Arco

et al. 2024

Interdiscip Sci Comput Life Sci

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The electrical activity of the neural processes involved in cognitive functions is captured in EEG signals, allowing the exploration of the integration and coordination of neuronal oscillations across multiple spatiotemporal scales. We have proposed a novel approach that combines the transformation of EEG signal into image sequences, considering cross-frequency phase synchronisation (CFS) dynamics involved in low-level auditory processing, with the development of a two-stage deep learning model for the detection of developmental dyslexia (DD). This deep learning model exploits spatial and temporal information preserved in the image sequences to find discriminative patterns of phase synchronisation over time achieving a balanced accuracy of up to 83%. This result supports the existence of differential brain synchronisation dynamics between typical and dyslexic seven-year-old readers. Furthermore, we have obtained interpretable representations using a novel feature mask to link the most relevant regions during classification with the cognitive processes attributed to normal reading and those corresponding to compensatory mechanisms found in dyslexia. Graphical Abstract

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RETRACTED ARTICLE: Peripheral Blood Smear Images Classification for Acute Lymphoblastic Leukemia Diagnosis with an Improved Convolutional Neural Network

Cited by 26 publications

References 59 publications

Feature selection algorithm based on optimized genetic algorithm and the application in high-dimensional data processing

Feature selection algorithm based on optimized genetic algorithm and the application in high-dimensional data processing

A Hybrid Multi-population Optimization Algorithm for Global Optimization and Its Application on Stock Market Prediction

Unraveling Brain Synchronisation Dynamics by Explainable Neural Networks using EEG Signals: Application to Dyslexia Diagnosis

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