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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Indonesian Journal of Electrical Engineering and Computer Science
Sinan Salim Mohammed Sheet
Northern Technical University
Computer Science & IT

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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.
Microcontroller based in vitro hematocrit measurement system Sinan Salim Mohammed Sheet; Mohammed Sabah Jarjees
Indonesian Journal of Electrical Engineering and Computer Science Vol 18, No 2: May 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v18.i2.pp717-723

Abstract

The hematocrit (HCT) is the most important measurement in the blood profile. It has been used for early diagnose of the specific blood diseases such as anaemia, leukaemia and malaria. The microhematocrit is the conventional method of measurement of HCT manually which is time-consuming and uncertain due to human error. An automated system for measuring hematocrit will minimize the human-error and the time which will give the ability for medical staff to serve more patients. This paper aims to demonstrate an automated system for measuring the HCT based on microcontroller. The designed system based on Arduino Atmega 2560 microcontroller and combination array of lighting emitting diode and photodetectors. The transmission and the absorption characteristics of the red light (660nm) through the centrifuged blood sample in a capillary tube are calculated and used to determine the HCT. The outputs are analyzed to determine the haemoglobin (HB) and packed cell volume (PCV). The significant correlation (r=0.9856, p=3.106*10-4) between the PCV readings of the proposed system and the conventional method has been observed. The most important finding is the precise of PCV and HB readings for the proposed system compared with previous automated methods as well as the conventional method have been obtained.
Co-Authors Bassam Tahseen Ahmed Mohammed Sabah Jarjees