Leukemia Sub-Type Classification by Using Machine Learning Techniques on Gene Expression

Simsek, Ebru; Badem, Hasan; Okumuş, İbrahım

doi:10.1007/978-981-16-2102-4_56

Cited by 6 publications

(4 citation statements)

References 14 publications

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“…Various awareness meetings are held at national and state level to detect and prevent the disease. The researchers also emphasize the idea that village health nurses can be trained in primary health centers in villages to detect early disease impact due to the high prevalence of the disease [17].…”

Section: Literature Reviewmentioning

confidence: 99%

“…(Genes -The DNA type) If you have this disease from your mother or father in your family, it is very important to get tested without neglecting it [16]. This disease is spread by many chemical factors (carcinogens) such as tobacco, roofing asbestos, arsenic metal, radiation (Gamma and X Rays), excessive solar radiation, smoke emitted from vehicles [17]. For example, when we eat chicken that is fried in liters of oil in large oil pans, soaked in packets of masalas and fried to a red and fragrant color, we don't think about whether it is fresh oil rather than what kind of oil it is.…”

Section: Introductionmentioning

confidence: 99%

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deep DNA machine learning model to classify the tumor genome of patients with tumor sequencing

Logeshwaran¹,

Adhikari

Joshi³

et al. 2022

ijhs

156

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In general, the various medical systems currently available provide insights into changes in the tumor genome of patients with tumor sequencing. Most of the tumor DNA sequencing can also be referred to as genetic specification or genetic testing. The sequence results help clinical decision-making to develop a personalized cancer treatment plan based on the molecular characteristics of the tumor rather than a one-size-fits-all treatment approach. The tumor sequencing also plays a major role in cancer research. In this paper, an improved method based on machine learning was proposed to analyze the sequencing and tumor sequencing patterns of the human gene. This proposed method analyzes the circulatory problems of patients with different tumor types for analysis in the public domain. It also constantly monitors large data sets of cancer or tumor genetic sequences to calculate tumor size and location. This allows the doctor to get an accurate report on the type of tumor and the problems it can cause to the patient. The Analysis of these datasets of cancer tumor gene sequences reveals that the genetic makeup of each patient is different and that no two cancers are the same.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

deep DNA machine learning model to classify the tumor genome of patients with tumor sequencing

Logeshwaran¹,

Adhikari

Joshi³

et al. 2022

ijhs

156

View full text Add to dashboard Cite

show abstract

“…Several studies [17][18][19] have investigated incorporating Machine Learning (ML) into automated pathological diagnosis, especially with the increase in digitized microscopic images. Machine learning algorithms use characteristics such as morphology and size to recognize cell types and abnormalities, improving accurate classification and allowing pathologists to concentrate on intricate aspects of diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Advancing Early Leukemia Diagnostics: A Comprehensive Study Incorporating Image Processing and Transfer Learning

Haque,

Al Sakib,

Hossain

et al. 2024

BioMedInformatics

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Disease recognition has been revolutionized by autonomous systems in the rapidly developing field of medical technology. A crucial aspect of diagnosis involves the visual assessment and enumeration of white blood cells in microscopic peripheral blood smears. This practice yields invaluable insights into a patient’s health, enabling the identification of conditions of blood malignancies such as leukemia. Early identification of leukemia subtypes is paramount for tailoring appropriate therapeutic interventions and enhancing patient survival rates. However, traditional diagnostic techniques, which depend on visual assessment, are arbitrary, laborious, and prone to errors. The advent of ML technologies offers a promising avenue for more accurate and efficient leukemia classification. In this study, we introduced a novel approach to leukemia classification by integrating advanced image processing, diverse dataset utilization, and sophisticated feature extraction techniques, coupled with the development of TL models. Focused on improving accuracy of previous studies, our approach utilized Kaggle datasets for binary and multiclass classifications. Extensive image processing involved a novel LoGMH method, complemented by diverse augmentation techniques. Feature extraction employed DCNN, with subsequent utilization of extracted features to train various ML and TL models. Rigorous evaluation using traditional metrics revealed Inception-ResNet’s superior performance, surpassing other models with F1 scores of 96.07% and 95.89% for binary and multiclass classification, respectively. Our results notably surpass previous research, particularly in cases involving a higher number of classes. These findings promise to influence clinical decision support systems, guide future research, and potentially revolutionize cancer diagnostics beyond leukemia, impacting broader medical imaging and oncology domains.

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“…They capture the progress of technology and knowledge, allowing researchers to reinterpret and reanalyze the data using modern analytical techniques. One notable historical dataset is the work of Golub et al (Simsek, Badem, & Okumus, 2021), which focused on gene expression in acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) patients. This dataset, generated through DNA microarrays, paved the way for molecular cancer classification.…”

mentioning

confidence: 99%

Improving Acute Leukemia Classification through Recursive Feature Elimination and Multilayer Perceptron Analysis of Gene Expression Data

Ogunbiyi,

Adegoke,

Oluwatosin

et al. 2024

Int. J. Data. Science.

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This study presents an approach to improving the classification of acute leukemia subtypes using gene expression data analysis. Leveraging Recursive Feature Elimination (RFE) as a feature selection technique and Multilayer Perceptron (MLP) as the predictive modeling framework, this research aims to identify the most influential genes for distinguishing between Acute Lymphoblastic Leukemia (ALL) and Acute Myeloid Leukemia (AML) cases. RFE systematically ranks and selects the most discriminative gene attributes, while MLP constructs a predictive model based on these attributes. The results demonstrate the effectiveness of this combined approach, achieving precision, accuracy, F1-Score, and recall rates of approximately 99% for leukemia subtype classification. Furthermore, specific genes contributing most to the model's predictive power and shedding light on potential biomarkers for leukemia diagnosis were identified. This research underscores the significance of RFE and MLP in the analysis of gene expression data and their potential impact on clinical decision-making in the field of oncology.

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Leukemia Sub-Type Classification by Using Machine Learning Techniques on Gene Expression

Cited by 6 publications

References 14 publications

deep DNA machine learning model to classify the tumor genome of patients with tumor sequencing

deep DNA machine learning model to classify the tumor genome of patients with tumor sequencing

Advancing Early Leukemia Diagnostics: A Comprehensive Study Incorporating Image Processing and Transfer Learning

Improving Acute Leukemia Classification through Recursive Feature Elimination and Multilayer Perceptron Analysis of Gene Expression Data

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