ERYTHROCYTE CLASSIFICATION USING ALEXNET AND SIMPLE CNN

Harjoko, Agus and Tyas, Dyah Aruming and Hartati, Sri and Ratnaningsih, Tri (2023) ERYTHROCYTE CLASSIFICATION USING ALEXNET AND SIMPLE CNN. ICIC Express Letters, 17 (1). pp. 103-111. ISSN 1881-803X

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Abstract

One examination method to support thalassemia diagnosis is a blood morphological examination of the patient’s peripheral blood smear. However, manual analysis
of peripheral blood smears requires time, unique expertise, and expert eye fatigue. This
paper proposes a computer vision method using deep learning to assist experts in examining peripheral blood smears. The dataset consists of nine erythrocyte types that appear in
thalassemia patients. The image size normalization was conducted before the deep learning model used the image. Data augmentation was used to increase the number of data
in the datasets. The transfer learning approach is used to improve classification results. The erythrocyte classification result using Alexnet and simple CNN has been compared. The best performance of the Alexnet model reached 95.92% accuracy, 91.46% sensitivity, and 99.48% specificity

Item Type: Article
Uncontrolled Keywords: Erythrocyte classification, Deep learning, Thalassemia, Alexnet, CNN
Subjects: Q Science > QA Mathematics
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Mathematics and Natural Sciences > Computer Science & Electronics Department
Depositing User: Erlita Cahyaningtyas Cahyaningtyas
Date Deposited: 12 Sep 2024 04:09
Last Modified: 16 Sep 2024 09:06
URI: https://ir.lib.ugm.ac.id/id/eprint/3573

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