Automatic Classification and Diagnostic Analysis of Microscopic Blood Images Using R CNN Model

Based on model deep learning, according to the R-CNN model architecture, leukocyte, erythrocyte, and thrombocyte content in the blood is analyzed based on healthy blood composition, patients are divided into different classes and their susceptibility to the disease is predicted based on the criteria of time speed and disease accuracy index. conducted as a research objective. The results of the conducted research gave an effective indicator and gave up to 87% accuracy in identifying diseases.

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Published
2024-12-11
DOI
https://doi.org/10.1145/3726122.3726187
Primary Topic
Brain Tumor Detection and Classification
Type
article
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article

Automatic Classification and Diagnostic Analysis of Microscopic Blood Images Using R CNN Model

Fotima Tulaganova, Sanjar Omonov, Abdulaziz Xo'jamqulov
Brain Tumor Detection and Classification
article

Automatic Classification and Diagnostic Analysis of Microscopic Blood Images Using R CNN Model

Fotima Tulaganova, Sanjar Omonov, Abdulaziz Xo'jamqulov
article en

Abstract

Based on model deep learning, according to the R-CNN model architecture, leukocyte, erythrocyte, and thrombocyte content in the blood is analyzed based on healthy blood composition, patients are divided into different classes and their susceptibility to the disease is predicted based on the criteria of time speed and disease accuracy index. conducted as a research objective. The results of the conducted research gave an effective indicator and gave up to 87% accuracy in identifying diseases.

Tashkent State University of Economics (UZ)
Openalex Percentile: Top 66%
Brain Tumor Detection and Classification
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Automatic Classification and Diagnostic Analysis of Microscopic Blood Images Using R CNN Model — Fotima Tulaganova, Sanjar Omonov, et al. · (2024) | TGRS Research Map | TGRS