ACADSTAFF UGM
| Title | : | Erythrocyte classification using Alexnet and simple CNN |
| Author | : |
Prof. Drs. Agus Harjoko, M.Sc., Ph.D. (1) Dr. Dyah Aruming Tyas, S.Si. (2) Prof. Dra. Sri Hartati, M.Sc., Ph.D. (3) Dr. dr. Tri Ratnaningsih, Sp.PK(K).,M.Kes. (4) |
| Date | : | 0 2023 |
| Keyword | : | Erythrocyte classification,deep learning,thalassemia,Alexnet,CNN Erythrocyte classification,deep learning,thalassemia,Alexnet,CNN |
| 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 a lot of time, special 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 types of erythrocytes that appear in thalassemia patients. The image size normalization was conducted before the deep learning model uses the image. Data augmentation was undertaken 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 Alexnet model reached 95.92?curacy, 91.46% sensitivity, and 99.48% specificity. |
| Group of Knowledge | : | Ilmu Komputer |
| Original Language | : | English |
| Level | : | Internasional |
| Status | : |
Published
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| No | Title | Action |
|---|---|---|
| 1 |
el-17-01-11 published.pdf
Document Type : [PAK] Full Dokumen
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