An Efficient Algorithm for Acute Lymphoblastic Leukemia Quantification in Blood Cell Images Using Support Vector Machines Algorithm

Искандарова, Сайёра, Тулаганова, Фотима, Акбарова, Мохидил, Хаитов, Хайрулло

Рақамли технологияларнинг назарий ва амалий масалалари · 2024-yil

Annotatsiya

As the capabilities of information technologies develop, the effectiveness of using the capabilities of CNN convolutional neural networks, an algorithm with a high level of accuracy, and early prediction of various diseases in blood cell images is high. This study proposes an effective method for early detection of acute lymphoblastic leukemia in blood cell images using Support Vector Machines (SVM) algorithm. In this method, a pattern of cells is recognized and used to identify cell markers specific to leukemia. This algorithm is used to match leukemia to a single marker in the cell image. Comparing SNN convolutional neural network algorithms with random forest (RF), Bayesian classifier, Support Vector Machines (SVM) and K nearest neighbor (KNN) algorithms, the results obtained by Support Vector Machines (SVM) were found to be 90.9% efficient.

Maqola ma’lumotlari
MualliflarИскандарова, Сайёра, Тулаганова, Фотима, Акбарова, Мохидил, Хаитов, Хайрулло
JurnalРақамли технологияларнинг назарий ва амалий масалалари
Nashr sanasi2024-05-28
Jild7
Son2
Betlar27-33
TilIngliz
DOI10.62132/ijdt.v7i2.177

Kalit so‘zlar

случайный лес, байесовский классификатор, машины опорных векторов, K ближайший сосед, random forest, Bayesian classifier, Support Vector Machines, K nearest neighbor

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