Article Per Year (5 Year)
p-Index From 2020 - 2025
0.339
P-Index
This Author published in this journals
All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control)
Bassam Tahseen Ahmed
Northern Technical University
Computer Science & IT

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Leukocytes identification using augmentation and transfer learning based convolution neural network Mohammed Sabah Jarjees; Sinan Salim Mohammed Sheet; Bassam Tahseen Ahmed
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 20, No 2: April 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v20i2.23163

Abstract

Most haematological diseases can be diagnosed using the morphological analysis of the microscopic blood image. The basic routine of the morphological analysis can be performed using the microscopic device which requires the skills and experiences of the haematologists. An inexperienced haematologist can lead to critical human errors. Therefore, this paper aims to propose an automated classification system used to classify different types of leukocytes based on the convolution neural network (CNN) algorithm. CNN has achieved robust performance in various fields especially in medical applications. A dataset of microscopic blood cells images of the conforming tags (basophil, eosinophil, erythroblast, lymphocyte, monocyte, neutrophil, and platelet) was used to train and test the proposed algorithm. The augmentation and deep transfer approaches were used to improve and enhance the performance of the CNN algorithm. The overall accuracy of the proposed classifier was 98% with Visual Geometry Group-19 (VGG-19). The obtained accuracy was higher than the state-of-art algorithms. To conclude that using the augmentation and deep transfer approaches with VGG-19 can obtain better classification results.
Co-Authors Mohammed Sabah Jarjees Sinan Salim Mohammed Sheet