An efficient EGWO algorithm as feature selection for B-ALL diagnoses and its subtypes classification using peripheral blood smear images

Sallam, Nada M.; Saleh, Ahmed I.; Ali, H. Arafat; Abdelsalam, Mohamed M.

doi:10.1016/j.aej.2023.01.004

Cited by 12 publications

(11 citation statements)

References 39 publications

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“…With its rapid development, artificial intelligence has begun to play an important role in different engineering fields, such as in disease diagnosis [ 12 ]. The deep learning model has since emerged and become a widely studied research topic.…”

Section: Introductionmentioning

confidence: 99%

A Hyperspectral Image Classification Method Based on the Nonlocal Attention Mechanism of a Multiscale Convolutional Neural Network

Cao

et al. 2023

Sensors

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Recently, convolution neural networks have been widely used in hyperspectral image classification and have achieved excellent performance. However, the fixed convolution kernel receptive field often leads to incomplete feature extraction, and the high redundancy of spectral information leads to difficulties in spectral feature extraction. To solve these problems, we propose a nonlocal attention mechanism of a 2D–3D hybrid CNN (2-3D-NL CNN), which includes an inception block and a nonlocal attention module. The inception block uses convolution kernels of different sizes to equip the network with multiscale receptive fields to extract the multiscale spatial features of ground objects. The nonlocal attention module enables the network to obtain a more comprehensive receptive field in the spatial and spectral dimensions while suppressing the information redundancy of the spectral dimension, making the extraction of spectral features easier. Experiments on two hyperspectral datasets, Pavia University and Salians, validate the effectiveness of the inception block and the nonlocal attention module. The results show that our model achieves an overall classification accuracy of 99.81% and 99.42% on the two datasets, respectively, which is higher than the accuracy of the existing model.

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Section: Introductionmentioning

confidence: 99%

A Hyperspectral Image Classification Method Based on the Nonlocal Attention Mechanism of a Multiscale Convolutional Neural Network

Cao

et al. 2023

Sensors

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“…Cancers affecting blood cells occur more often in childhood than in adolescence [8]. Specifically, acute lymphoblastic leukemia accounts for 14% of all leukemia diagnosed in people under 75, while acute myeloid leukemia accounts for 15-20% [9].…”

Section: Introductionmentioning

confidence: 99%

Pattern Recognition of Acute Lymphoblastic Leukemia (ALL) Using Computational Deep Learning

et al. 2023

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Leukemia is a cancer of blood-producing cells, including the bone marrow. Abnormal white blood cells travel through blood vessels and multiply rapidly. Healthy cells in the body become a minority, and the imbalance increases the chances of infection in the body. Leukemia or blood cancer is the most common cancer in children ages 2 -14. Most leukemia in children is treated. Acute lymphocytic leukemia (ALL) is a type of cancer in the blood and bone marrow. It progresses rapidly when immature white blood cells are formed instead of mature ones. Treatments for acute lymphocytic leukemia include drugs and blood transfusions directly into veins, chemotherapy, and all transplantation, which involve transferring organs or tissues within the body or from one person to another. In this paper, Pattern Recognition of Acute Lymphoblastic Leukemia has been proposed using Computational Deep Learning. Pattern recognition technology uses mathematical algorithms to identify patterns in large datasets of data. Analyzing the data, the algorithms can identify patterns indicative of certain states or conditions. In the case of ALL, the algorithm would look for patterns in white blood cell count data that indicate the presence of ALL. These patterns may include changes in the number of white blood cells over time, changes in the composition of the white blood cells, or changes in the levels of certain proteins or gene expressions associated with ALL. The proposed ALLDM model achieved 81.53% (DDS) and 87.92% (SDS) of chemotherapy management, 79.16% (DDS) and 94.31% (SDS) of Stem Cell Transplantation Management, 63.77% (DDS) and 87.37% (SDS) of Radiation therapy Management and 88.92% (DDS) and 85.86% (SDS) of Targeted therapy drugs management.

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“…Machine learning (ML) algorithms have been widely used in various areas of health sciences in the last two decades. Their ability to process large amounts of multidimensional data and interpret the mutual influence of a large number of variables means that they can be used for more efficient analysis of medical data and early diagnosis [ 28 , 29 , 30 ]. The application of ML in drug delivery has become increasingly prominent with the development of more complex techniques such as nanosystems or various approaches to personalized medicine, including 3D printing [ 31 , 32 ].…”

Section: Introductionmentioning

confidence: 99%

Application of Machine-Learning Algorithms for Better Understanding the Properties of Liquisolid Systems Prepared with Three Mesoporous Silica Based Carriers

et al. 2023

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The processing of liquisolid systems (LSS), which are considered a promising approach to improving the oral bioavailability of poorly soluble drugs, has proven challenging due to the relatively high amount of liquid phase incorporated within them. The objective of this study was to apply machine-learning tools to better understand the effects of formulation factors and/or tableting process parameters on the flowability and compaction properties of LSS with silica-based mesoporous excipients as carriers. In addition, the results of the flowability testing and dynamic compaction analysis of liquisolid admixtures were used to build data sets and develop predictive multivariate models. In the regression analysis, six different algorithms were used to model the relationship between tensile strength (TS), the target variable, and eight other input variables. The AdaBoost algorithm provided the best-fit model for predicting TS (coefficient of determination = 0.94), with ejection stress (ES), compaction pressure, and carrier type being the parameters that influenced its performance the most. The same algorithm was best for classification (precision = 0.90), depending on the type of carrier used, with detachment stress, ES, and TS as variables affecting the performance of the model. Furthermore, the formulations with Neusilin® US2 were able to maintain good flowability and satisfactory values of TS despite having a higher liquid load compared to the other two carriers.

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An efficient EGWO algorithm as feature selection for B-ALL diagnoses and its subtypes classification using peripheral blood smear images

Cited by 12 publications

References 39 publications

A Hyperspectral Image Classification Method Based on the Nonlocal Attention Mechanism of a Multiscale Convolutional Neural Network

A Hyperspectral Image Classification Method Based on the Nonlocal Attention Mechanism of a Multiscale Convolutional Neural Network

Pattern Recognition of Acute Lymphoblastic Leukemia (ALL) Using Computational Deep Learning

Application of Machine-Learning Algorithms for Better Understanding the Properties of Liquisolid Systems Prepared with Three Mesoporous Silica Based Carriers

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