Leukemia Diagnosis Based on Machine Learning Algorithms

Babaso, S. Patil; Mishra, Sonali; Junnarkar, Aparna A.

doi:10.1109/inocon50539.2020.9298321

Cited by 8 publications

(3 citation statements)

References 11 publications

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“…The training objective is to minimize a combined loss function, which includes the focal loss (Equation ( 18)) for heatmap prediction and the smooth L1 loss for bounding box regression. The smooth L1 loss is shown in Equation (19). Where, (N) is the number of positive samples, y ij is the binary indicator for the presence of class (j), p ij is the predicted probability for class (j), t ij and t ij are the ground truth and predicted bounding box coordinates, respectively.…”

Section: Centernetmentioning

confidence: 99%

“…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%

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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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Section: Centernetmentioning

confidence: 99%

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

View full text Add to dashboard Cite

show abstract

“…To identify the kind of leukemia from blood smear pictures, they adopted a support vector system based on radial kernels [13]. To identifying the characteristics of these cancerous cells, other studies also employed a few different ML models [14][15][16]. Several ML-based models for leukemia detection and classification are presented in depth by the authors in their review paper.…”

Section: Literature Surveymentioning

confidence: 99%

A Comprehensive Assessment and Classification of Acute Lymphocytic Leukemia

Bose,

Bandyopadhyay

2024

MCA

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Leukemia is a form of blood cancer that results in an increase in the number of white blood cells in the body. The correct identification of leukemia at any stage is essential. The current traditional approaches rely mainly on field experts’ knowledge, which is time consuming. A lengthy testing interval combined with inadequate comprehension could harm a person’s health. In this situation, an automated leukemia identification delivers more reliable and accurate diagnostic information. To effectively diagnose acute lymphoblastic leukemia from blood smear pictures, a new strategy based on traditional image analysis techniques with machine learning techniques and a composite learning approach were constructed in this experiment. The diagnostic process is separated into two parts: detection and identification. The traditional image analysis approach was utilized to identify leukemia cells from smear images. Finally, four widely recognized machine learning algorithms were used to identify the specific type of acute leukemia. It was discovered that Support Vector Machine (SVM) provides the highest accuracy in this scenario. To boost the performance, a deep learning model Resnet50 was hybridized with this model. Finally, it was revealed that this composite approach achieved 99.9% accuracy.

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