Lightweight Classification of Normal Versus Leukemic Cells Using Feature Extraction

Sant’Anna, Yúri Faro Dantas de; Oliveira, José Elwyslan Maurício de; Dantas, Daniel Oliveira

doi:10.1109/iscc53001.2021.9631499

Cited by 2 publications

(2 citation statements)

References 29 publications

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“…Illumination variations of the cytoplasm and nucleus must be handled with care because they significantly impact the model's performance [107]. In addition, the object detection deep learning model used by previous researchers is so complex that it is inefficient in terms of energy consumption and embedded device utilization [108]. The use of deep learning to detect ALL in microscopic blood images is still far from perfect and leaves a great deal of room for future research advancement.…”

Section: A Challengesmentioning

confidence: 99%

“…This in itself is a challenge, as not every region or health agency is equipped with computers with high specifications and qualified resources [36]. A simple model with an accuracy result that does not deviate from the state-of-the-art model or even surpasses it becomes a challenge for researchers so that the model has a high level of application in the real world [108]. Several studies have centered on creating models that are easy to use on mobile and embedded devices.…”

Section: ) the Complexity Of The Model Is Highmentioning

confidence: 99%

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Deep Learning for the Detection of Acute Lymphoblastic Leukemia Subtypes on Microscopic Images: A Systematic Literature Review

2023

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Computer vision research in detecting and classifying the subtype Acute Lymphoblastic Leukemia (ALL) has contributed to computer-aided diagnosis with improved accuracy. Another contribution is to serve as an assistant and second opinion for doctors and hematologists in diagnosing the ALL subtype.Early detection can also rely on computer-aided diagnosis to determine initial treatment. The purpose of this study is to review the progress of research in the detection and classification of ALL subtypes. The method's discussion focuses on the application of deep learning to the domain of object detection and classification. Motivations, challenges, and future research recommendations are thoroughly discussed to improve understanding and progress in this field of study. The study was carried out methodically by analyzing a collection of papers on the detection and classification of ALL subtypes published in science direct, IEEE, and PubMed from 2018 to 2022. The analysis of this paper field is included in the results of the selected paper. The paper selection from among 65 papers was based on inclusion and exclusion methods. Based on research methods and objectives, papers are divided into two large groups. The first group discusses the classification of ALL subtypes, while the second group discusses the detection of ALL subtypes. The discussion of prior research reveals some challenging issues and future work, such as the limited availability of the ALL subtypes dataset, the high computational complexity of the deep learning model, and further exploration of transformers in computer vision as a reference for research gaps that can contribute to future research.

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Section: A Challengesmentioning

confidence: 99%

Section: ) the Complexity Of The Model Is Highmentioning

confidence: 99%

Deep Learning for the Detection of Acute Lymphoblastic Leukemia Subtypes on Microscopic Images: A Systematic Literature Review

2023

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Interpretable Lightweight Ensemble Classification of Normal versus Leukemic Cells

2022

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The lymphocyte classification problem is usually solved by deep learning approaches based on convolutional neural networks with multiple layers. However, these techniques require specific hardware and long training times. This work proposes a lightweight image classification system capable of discriminating between healthy and cancerous lymphocytes of leukemia patients using image processing and feature-based machine learning techniques that require less training time and can run on a standard CPU. The features are composed of statistical, morphological, textural, frequency, and contour features extracted from each image and used to train a set of lightweight algorithms that classify the lymphocytes into malignant or healthy. After the training, these classifiers were combined into an ensemble classifier to improve the results. The proposed method has a lower computational cost than most deep learning approaches in learning time and neural network size. Our results contribute to the leukemia classification system, showing that high performance can be achieved by classifiers trained with a rich set of features. This study extends a previous work by combining simple classifiers into a single ensemble solution. With principal component analysis, it is possible to reduce the number of features used while maintaining a high accuracy.

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Lightweight Classification of Normal Versus Leukemic Cells Using Feature Extraction

Cited by 2 publications

References 29 publications

Deep Learning for the Detection of Acute Lymphoblastic Leukemia Subtypes on Microscopic Images: A Systematic Literature Review

Deep Learning for the Detection of Acute Lymphoblastic Leukemia Subtypes on Microscopic Images: A Systematic Literature Review

Interpretable Lightweight Ensemble Classification of Normal versus Leukemic Cells

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