Convergent learning–based model for leukemia classification from gene expression

Mallick, Pradeep Kumar; Mohapatra, Saumendra Kumar; Chae, Gyoo‐Soo; Mohanty, Mihir Narayan

doi:10.1007/s00779-020-01467-3

Cited by 20 publications

(8 citation statements)

References 33 publications

(42 reference statements)

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“…To select the most palatable characteristics, they used the social spider optimization algorithm (SSOA). The deep learning approach was utilized by Mallick et al (2020) to classify the various kinds of leukemia. They used deep neural networks (DNNs) to classify the gene expression data.…”

Section: Related Workmentioning

confidence: 99%

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A robust classification of acute lymphocytic leukemia‐based microscopic images with supervised Hilbert‐Huang transform

Elrefaie,

Mohamed,

Marzouk

et al. 2023

Microscopy Res & Technique

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Acute lymphocytic leukemia (ALL) is a malignant condition characterized by the development of blast cells in the bone marrow and their quick dissemination into the bloodstream. It primarily affects children and individuals over the age of 60. Manual blood testing, which has been around for a long time, may be slow. The likelihood of recognizing ALL in its early stages was increased by automating the diagnosis. This research developed an improved criterion for classifying ALL microscopic images into two categories: normal images and blast images. First, to save processing time, innovative image preprocessing techniques were employed to gather data for data augmentation, enhancement, and conversion. The K‐means clustering technique was also utilized to effectively segment the relevant nuclei from the background. Furthermore, the most salient features were extracted using an empirical mode decomposition (EMD) based on the Hilbert‐Huang transform. MATLAB functions such as principal component analysis, gray level co‐occurrence matrix, local binary pattern, shape features, discrete cosine transform, discrete Fourier transform, discrete wavelet transform, and independent component analysis have been used and compared with EMD. The Bayesian regularization (BR) method has been implemented in the neural networks (NNs) classifier. Along with NNs, other classifiers such as support vector machine, K‐nearest neighbors, random forest, naive Bayes, logistic regression, and decision tree have been used, evaluated, and contrasted with NNs. According to experimental findings, the ALL‐IDB2 (Image Database 2) dataset's NNs‐based‐EMD model classified objects with an accuracy of 98.7%, sensitivity of 99.3%, and specificity of 98.1%.Research Highlights Implement a robust method for classifying normal and blast ALL images in the state of the art using the combination of the BR algorithm and the neural networks classifier. Perform robust data processing via data augmentation and conversion from RGB (Red, Green, and Blue) image LAB (Luminosity, A: color space, B: color space) image. Extract the nuclei correctly from the background image using k‐means clustering. Extract the most salient features from the segmented images using EMD in the state of the art of HHT.

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Section: Related Workmentioning

confidence: 99%

“…To select the most palatable characteristics, they used the social spider optimization algorithm (SSOA). The deep learning approach was utilized by Mallick et al (2020)…”

Section: Research Highlightsmentioning

confidence: 99%

A robust classification of acute lymphocytic leukemia‐based microscopic images with supervised Hilbert‐Huang transform

Elrefaie,

Mohamed,

Marzouk

et al. 2023

Microscopy Res & Technique

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“…Mallick et al [133] have presented five-layered deep CNN-based ALL and AML classification model. Roy and Ameer [84] have used AlexNet [91] to classify subtypes of WBC successfully.…”

Section: Figure 11 Residual Learning Blockmentioning

confidence: 99%

A Systematic Review on Recent Advancements in Deep and Machine Learning Based Detection and Classification of Acute Lymphoblastic Leukemia

et al. 2022

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Automatic Leukemia or blood cancer detection is a challenging job and is very much required in healthcare centers. It has a significant role in early diagnosis and treatment planning. Leukemia is a hematological disorder that starts from the bone marrow and affects white blood cells (WBCs). Microscopic analysis of WBCs is a preferred approach for an early detection of Leukemia since it is cost-effective and less painful. Very few literature reviews have been done to demonstrate a comprehensive analysis of deep and machine learning-based Acute Lymphoblastic Leukemia (ALL) detection. This article presents a systematic review of the recent advancements in this knowledge domain. Here, various artificial intelligence-based ALL detection approaches are analyzed in a systematic manner with merits and demits. The review of these schemes is conducted in a structured manner. For this purpose, segmentation schemes are broadly categorized into signal and image processing-based techniques, conventional machine learning-based techniques, and deep learning-based techniques. Conventional machine learning-based ALL classification approaches are categorized into supervised and unsupervised machine learning is presented. In addition, deep learning-based classification methods are categorized into Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and the Autoencoder. Then, CNN-based classification schemes are further categorized into conventional CNN, transfer learning, and other advancements in CNN. A brief discussion of these schemes and their importance in ALL classification are also presented. Moreover, a critical analysis is performed to present a clear idea about the recent research in this field. Finally, various challenging issues and future scopes are discussed that may assist readers in formulating new research problems in this domain.

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“…At this time, if the operation is continued for 5 seconds or longer, the overload protection function of the driver operates to stop the motor naturally. Figure 2 shows the torque characteristics according to the rotation speed [15,16].…”

Section: Bldc Motor Characteristicmentioning

confidence: 99%

Development of Drone Mounted BLDC Motor Stator using Multi Stacked Structure of Cartridge

et.al¹

2021

TURCOMAT

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Received:11 november 2020; Accepted: 27 December 2020; Published online: 05 April 2021 Abstract: Drones are used in agriculture and external control, utilizing cameras, various sensors, and autonomous flight functions. However, it is difficult to fly for a long time because of flying with heavy equipment. Therefore, it is necessary to increase the flight time through lightening of the drone itself and efficient control of the BLDC motor. That is, it is necessary to improve the motor itself, which accounts for most of the energy consumption in drone flight. Stator fabrication of existing BLDC motors is done by winding copper wires through individual processes on the stator core through an automated process. However, in the case of an ultra-small BLDC motor, the stator core has a small size and is manufactured using a thin-diameter copper conductor, and thus has a lot of problems in the automation process. Therefore, in this paper, the multi-division coupling structure method of individual teeth of the stator was applied through a new type of BLDC motor stator design. That is, the cartridge type BLDC motor stator was applied to improve and reduce the BLDC motor stator method. Through this, the performance limit of the BLDC motor was improved by combining individual teeth with multiple cartridges. The cartridge type BLDC motor is manufactured by stacking several cartridges on each tooth. Therefore, various performances can be produced according to the connection state of the cartridges

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Convergent learning–based model for leukemia classification from gene expression

Cited by 20 publications

References 33 publications

A robust classification of acute lymphocytic leukemia‐based microscopic images with supervised Hilbert‐Huang transform

A robust classification of acute lymphocytic leukemia‐based microscopic images with supervised Hilbert‐Huang transform

A Systematic Review on Recent Advancements in Deep and Machine Learning Based Detection and Classification of Acute Lymphoblastic Leukemia

Development of Drone Mounted BLDC Motor Stator using Multi Stacked Structure of Cartridge

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