Automated analysis of microscopy images using deep convolutional neural networks

Yaser Banadaki; Adetayo Okunoye; Sanjay Batra; Eduardo Martinez; Shuju Bai; Safura Sharifi

doi:10.1117/12.2584497

27 April 2021 Automated analysis of microscopy images using deep convolutional neural networks

Yaser Banadaki, Adetayo Okunoye, Sanjay Batra, Eduardo Martinez, Shuju Bai, Safura Sharifi

Proceedings Volume 11593, Health Monitoring of Structural and Biological Systems XV; 115932X (2021) https://doi.org/10.1117/12.2584497
Event: SPIE Smart Structures + Nondestructive Evaluation, 2021, Online Only

Conference Poster

Abstract

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.

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Citation Download Citation

Yaser Banadaki, Adetayo Okunoye, Sanjay Batra, Eduardo Martinez, Shuju Bai, and Safura Sharifi "Automated analysis of microscopy images using deep convolutional neural networks", Proc. SPIE 11593, Health Monitoring of Structural and Biological Systems XV, 115932X (27 April 2021); https://doi.org/10.1117/12.2584497

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