Automated Analysis of Microscopy Images using Deep Convolutional Neural Networks

被引：0

作者：

Banadaki, Yaser ^{[1
]}

Okunoye, Adetayo ^{[2
]}

Batra, Sanjay ^{[1
]}

Martinez, Eduardo ^{[1
]}

Bai, Shuju ^{[3
]}

Sharifi, Safura ^{[4
]}

机构：

[1] Southern Univ, Dept Comp Sci, Baton Rouge, LA 70813 USA

[2] Univ Georgia, Dept Comp Sci, Athens, GA 30602 USA

[3] Clayton State Univ, Dept Comp Sci & Informat Technol, Morrow, GA 30260 USA

[4] Univ Illinois, Dept Phys, Champaign, IL 61820 USA

来源：

HEALTH MONITORING OF STRUCTURAL AND BIOLOGICAL SYSTEMS XV | 2021年 / 11593卷

关键词：

Cell Identification; Deep Convolutional Neural Networks; Microscopy Images; Learning algorithms; CLASSIFICATION;

D O I：

10.1117/12.2584497

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

The general cell quantification and identification have technical limitations concerning the fast and accurate detection of complex morphological cells, especially for overlapping cells, irregular cell shapes, bad focal planes, among other factors. We use the deep convolutional neural networks (DCNN) to classify the annotated images of five types of white blood cells. The accuracy and performance of the proposed framework are evaluated for the blood cell classifications. The results demonstrate that the DCNN model performs close to the accuracy of 80% and provides an accurate and fast method for hematological laboratories.

引用

页数：6

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