Bone marrow cells recognition methods in the diagnosis of minimal residual disease

Nikitaev, V. G.; Pronichev, A. N.; Polyakov, E. V.; Чернышева, О. А.; Serebryakova, I. N.; Тупицын, Н. Н.

doi:10.1016/j.procs.2020.02.229

Cited by 6 publications

(4 citation statements)

References 13 publications

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“…Table 4 demonstrates that the proposed approach achieved higher accuracy than in [16, 18, 22] based on the kNN classifier technique.…”

Section: Resultsmentioning

confidence: 94%

“…In addition, the kNN classifier technique provides higher accuracy rate in comparison with Naïve Bayes. Furthermore, the proposed approach is compared to other existing approaches in [16, 18, 22] and the results are presented in Tables 4 and 5.…”

Section: Resultsmentioning

confidence: 99%

“…Colour and texture features were extracted from the segmented nucleus, and finally, different classifier techniques such as kNN, SVM, and random forest have been used to classify the WBCs into normal and abnormal. As a result, the accuracy rate for each of the classifiers was reached 89, 85, and 80%, respectively[22]. Bodzas et al also developed an algorithm, in 2020, for leukaemia identification in which introduces a three‐phase filtration algorithm to achieve the best segmentation process [23].…”

Section: Literature Reviewmentioning

confidence: 99%

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Efficient computer‐aided diagnosis technique for leukaemia cancer detection

Abdulla

2020

IET image process

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Computer‐aided diagnosis (CAD) is a common tool for the detection of diseases, particularly different types of cancers, based on medical images. Digital image processing thus plays a significant role in the processing and analysis of medical images for diseases identification and detection purposes. In this study, an efficient CAD system for the acute lymphoblastic leukaemia (ALL) detection is proposed. The proposed approach entails two phases. In the first phase, the white blood cells (WBCs) are segmented from the microscopic blood image. The second phase involves extracting important features, such as shape and texture features from the segmented cells. Eventually, on the extracted features, Naïve Bayes and k‐nearest neighbour classifier techniques are implemented to identify the segmented cells into normal and abnormal cells. The performance of the proposed approach has been assessed through comprehensive experiments carried out on the well‐known ALL‐IDB data set of microscopic blood images. The experimental results demonstrate the superior performance of the proposed approach over the state‐of‐the‐art in terms of accuracy rate in which achieved 98.7%.

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“…Table 4 demonstrates that the proposed approach achieved higher accuracy than in [16, 18, 22] based on the kNN classifier technique.…”

Section: Resultsmentioning

confidence: 94%

Section: Resultsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Efficient computer‐aided diagnosis technique for leukaemia cancer detection

Abdulla

2020

IET image process

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“…Combination Therapies (33)(34)(35)-Researchers are exploring the use of combination therapies that involve different drugs or treatment approaches to improve outcomes and reduce resistance. Minimal Residual Disease (MRD) Monitoring (36,37)-Advances in MRD detection allow for more sensitive monitoring of treatment response. This helps doctors assess the effectiveness of treatment and make informed decisions about further interventions.…”

Section: Introductionmentioning

confidence: 99%

A di-electrophoretic simulation procedure of iron-oxide micro-particle drug attachment system for leukemia treatment using COMSOL software: a potential treatment reference for LMICs

Kiwumulo,

Muwonge,

Ibingira

et al. 2023

Front. Med. Technol.

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BackgroundLeukemia encompasses various subtypes, each with unique characteristics and treatment approaches. The challenge lies in developing targeted therapies that can effectively address the specific genetic mutations or abnormalities associated with each subtype. Some leukemia cases may become resistant to existing treatments over time making them less susceptible to chemotherapy or other standard therapies.ObjectiveDeveloping new treatment strategies to overcome resistance is an ongoing challenge particularly in Low and Middle Income Countries (LMICs). Computational studies using COMSOL software could provide an economical, fast and resourceful approach to the treatment of complicated cancers like leukemia.MethodsUsing COMSOL Multiphysics software, a continuous flow microfluidic device capable of delivering anti-leukemia drugs to early-stage leukemia cells has been computationally modeled using dielectrophoresis (DEP).ResultsThe cell size difference enabled the micro-particle drug attachment to the leukemia cells using hydrodynamic focusing from the dielectrophoretic force. This point of care application produced a low voltage from numerically calculated electrical field and flow speed simulations.ConclusionTherefore, such a dielectrophoretic low voltage application model can be used as a computational treatment reference for early-stage leukemia cells with an approximate size of 5 μm.

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