TELKOMNIKA Telecommunication, Computing, Electronics and Control
Leukocytes identification using augmentation and transfer learning based convolution neural network

TELKOMNIKA Telecommunication, Computing, Electronics and Control
Leukocytes identification using augmentation and transfer learning based convolution neural network

Convolution neural network
Haematological diseases
Microscopic blood images
VGG-19

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.

Mohammed Sabah Jarjees, Sinan Salim Mohammed Sheet, Bassam Tahseen Ahmed

Universitas Ahmad Dahlan

April 2022

Sri Wahyuni

ISSN: 1693-6930

PDF

English

Text