Detection of Anemia Based on Conjunctival Images Using a Convolutional Neural Network (CNN) Method
DOI:
https://doi.org/10.30872/jim.v21i1.28906Keywords:
Anemia, Conjungtiva, Convolutional Neural Network, MediaPipeAbstract
A hemoglobin level below 12 g/dL is the primary indicator of anemia, a condition commonly found in adolescent girls. Laboratory blood tests, as a conventional detection method, are invasive, time-consuming, and costly. This study developed a non-invasive classification system for anemia and non-anemia based on conjunctival images using a Convolutional Neural Network (CNN), implemented on a real-time website. A total of 433 conjunctival images were collected comprising 206 images of anemia and 227 of non-anemia sourced from smartphone cameras and the Kaggle dataset, divided in an 80:10:10 ratio for training, validation, and testing. Preprocessing included resizing to 150 150 pixels, augmentation (flip, rotation, zoom, translation, brightness), and pixel normalization. The CNN architecture consists of three convolutional layers (32, 64, and 128 filters), max pooling, dropout, and a fully connected layer with sigmoid activation, trained using the Adam optimizer and the binary cross-entropy loss function until the 43rd epoch. The model achieved an accuracy of 88.37%, precision of 0.89, recall of 0.88, and an F1-score of 0.88. The model was integrated with a Flask-based REST API and MediaPipe Face Landmarker to automatically detect the conjunctival Region of Interest (ROI) via camera or uploaded images, thereby potentially serving as a fast, practical, and easily accessible tool for the initial screening of anemia among adolescent girls in schools and primary health care facilities.
References
Abadi, A. N., & Wibowo, A. T. (2021). Classification of Anthurium Species Based on Leaf Images Using a Convolutional Neural Network. EProceedings of Engineering, 8(4), 4152–4170. https://openlibrarypublications.telkomuniversity.ac.id/index.php/engineering/article/view/15243
Adriyanto, T., Ramadhani, R. A., Helilintar, R., & Ristyawan, A. (2022). Classification of Dog and Cat Images using the CNN Method. ILKOM Jurnal Ilmiah, 14(3), 203–208. https://doi.org/10.33096/ilkom.v14i3.1116.203-208
Amalia, E., Lamada, M., Kaswar, A. B., Andayani, D. D., Studi, P., Komputer, T., Makassar, U. N., Makassar, U. N., Studi, P., Komputer, T., Makassar, U. N., Makassar, U. N., Kesehatan, D., Indonesia, R., Network, C. N., & Konjungtiva, P. (2023). Classification of Anemia Based on Palpebral Conjunctival Images Using Transfer Leraning Algorithm. 20(2), 128–134.
Ariyanto, R., & Prasetyo, H. (2022). Analysis of Color Space Transformation in Computer Vision-Based Object Identification. Jurnal Teknologi Informasi Dan Multimedia, 4(1).
Bintoro, P., Ratnasari, Wihardjo, E., Putri, I. P., & Asari, A. (2024). Introduction to Machine Learning. PT MAFY MEDIA LITERASI INDONESIA.
Chandel, R., Bhowmick, R., & Hariharan, U. (2023). A Comparison of Face Landmark Detection Techniques. 2023 4th International Conference on Computation, Automation and Knowledge Management, ICCAKM 2023, Iccakm, 1–6. https://doi.org/10.1109/ICCAKM58659.2023.10449635
Eriana, E. S., & Zein, A. (2023). Artificial Intelligence – AI. Encyclopedia of Digital Agricultural Technologies, 84–84. https://doi.org/10.1007/978-3-031-24861-0_300007
Fatmawati, U. D., Praditasari, W. A. A., & Aprilliyani, R. (2023). Application of the HSV - TCA Method for Real-Time Detection of Rice Weevils (Sitophylus Oryzae L). Jurnal Sistem Dan Teknologi Informasi (JustIN), 11(2), 277. https://doi.org/10.26418/justin.v11i2.56039
Helmyati, S., Hasanah, F. C., Putri, F., Sundjaya, T., & Dilantika, C. (2023). Biochemistry Indicators for the Identification of Iron Deficiency Anemia in Indonesia: A Literature Review. Amerta Nutrition, 7(3), 62–70. https://doi.org/10.20473/amnt.v7i3SP.2023.62-70
Indiranjani, N. (2023). Detection of Drowsiness on Employees' Faces Using the MediaPipe Face Detector and MobileNet. Bussiness Law Binus, 7(2), 33–48.
König, M. (2022). A sigmoid-optimized encoder – decoder network for crack segmentation with copy-edit-paste transfer learning. Computer-Aided Civil and Infrastructure Engineering, 37(14), 1875–1890. https://doi.org/10.1111/mice.12844
Lodia Tuturop, K., Martina Pariaribo, K., Asriati, A., Adimuntja, N. P., & Nurdin, M. A. (2023). Prevention of Anemia in Adolescent Girls: Students at the Faculty of Public Health, Cendrawasih University. Panrita Inovasi: Jurnal Pengabdian Kepada Masyarakat, 2(1), 19. https://doi.org/10.56680/pijpm.v2i1.46797
Lubis, D. R., & Angraeni, L. (2022). Early Detection of Anemia Through Hemoglobin Level Testing in Adolescent Girls. Jurnal Pengabdian Masyarakat Parahita (JPMP), 03, 24–35. https://doi.org/https://doi.org/10.54771/jpmbp.v3i01.377
Magdalena, R., Saidah, S., Da, I., Fuadah, Y. N., Herman, N., & Ibrahim, N. (2022). Convolutional Neural Network For Anemia Detection Based On Conjungtiva Palpebral Images. 3(2), 0–5.
Muhammad, S., & Wibowo, A. T. (2021). Classification of Aglaonema Plants Based on Leaf Images Using the Convolutional Neural Network (CNN) Method. E-Proceeding of Engineering, 8(5), 10621–10636.
Muqsith, F. I., Supriyati, E., & Listyorini, T. (2025). Android-Based Classification of Hijaiyah Letter Pronunciation Using CNN with Mel-Spectrogram Features. 10(1), 67–78. https://doi.org/10.30591/jpit.v10i1.8145
Pradnyawati, L. G. (2024). A Program to Revitalize the Distribution of Iron-Folic Acid Tablets for Stunting Prevention at Kintamani State High School 1.. Warmadewa Minestrium Medical Journal, 03, 194–199. https://doi.org/https://doi.org/10.22225/wmmj.3.3.2024.194-199
Purba, M. E., Situmorang, A. Z., Laurent, G., Ginting, B., Wahyu, M., Lubis, P., & Sinaga, F. M. (2025). Classification of Organic and Inorganic Waste Using a CNN Algorithm. 26(1), 37–54.
Purwandari, E. P., Andreswari, D., & Faraditha, U. (2021). Color and Texture Feature Extraction for the Retrieval of Besurek Batik Images. Pseudocode, 7(1), 17–25. https://doi.org/10.33369/pseudocode.7.1.17-25
Ramadhani, I. R., Nilogiri, A., & Qurrota, A. (2022). Classification of Plant Species Based on Leaf Images Using the Convolutional Neural Network Method. Jurnal Smart Teknologi, 3(3), 249–260. http://jurnal.unmuhjember.ac.id/index.php/JST
Setiawan, W. (2021). Deep Learning Using Convolutional Neural Networks: Theory and Applications. Media Nusa Creative (MNC Publishing). https://books.google.co.id/books?id=sE9LEAAAQBAJ
Tri Laksono, A., Citra Digital Buah, P., Wanarti Rusmamto, P., & Syariffuddien Zuhrie, M. (2022). Digital Image Processing of Mulberries Using the LDA (Linear Discriminant Analysis) Algorithm. Indonesian Journal of Engineering and Technology, 4(2), 71–78. https://journal.unesa.ac.id/index.php/inajet
Wandini, P., Widya Astuti, A., & Sayudin, S. (2024). Conjunctivitis: A Study of Anatomy, Histology, and Etiology in Eye Health. Jurnal Locus Penelitian Dan Pengabdian, 3(1), 79–91. https://doi.org/10.58344/locus.v3i1.2418
Wardani, K. R., & Leonardi, L. (2023). Classification of Grape Leaf Diseases Using a Convolutional Neural Network. Jurnal Tekno Insentif, 17(2), 112–126. https://doi.org/10.36787/jti.v17i2.1130
Wicaksono, S., & Nugroho, A. (2024). Improving Medical Image Quality Using Histogram Equalization for Deep Learning Model Optimization. Jurnal Nasional Teknologi Dan Sistem Informasi (TEKNOSI).
Widyaningrum, R., & Setiyaningrum, Z. (2024). The Relationship Between Dietary Patterns and the Incidence of Anemia Among Adolescent Girls at SMK Batik 2 Surakarta. Jurnal Ilmiah Universitas Batanghari Jambi, 24(3), 2174. https://doi.org/10.33087/jiubj.v24i3.5688
Downloads
Published
Issue
Section
License
Copyright Transfer StatementThe copyright of this article is transferred to Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer and when the article is accepted for publication. the authors transfer all and all rights into and to paper including but not limited to all copyrights in the Informatika Mulawarman. The author represents and warrants that the original is the original and that he/she is the author of this paper unless the material is clearly identified as the original source, with notification of the permission of the copyright owner if necessary. The author states that he has the authority and authority to make and carry out this task.
The author states that:
- This paper has not been published in the same form elsewhere.
- This will not be submitted elsewhere for publication prior to acceptance/rejection by this Journal.
A Copyright permission is obtained for material published elsewhere and who require permission for this reproduction. Furthermore, I / We hereby transfer the unlimited publication rights of the above paper to Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer. Copyright transfer includes exclusive rights to reproduce and distribute articles, including reprints, translations, photographic reproductions, microforms, electronic forms (offline, online), or other similar reproductions.
The author's mark is appropriate for and accepts responsibility for releasing this material on behalf of any and all coauthor. This Agreement shall be signed by at least one author who has obtained the consent of the co-author (s) if applicable. After the submission of this agreement is signed by the author concerned, the amendment of the author or in the order of the author listed shall not be accepted.
Rights / Terms and Conditions Saved
- The author keeps all proprietary rights in every process, procedure, or article creation described in Work.
- The author may reproduce or permit others to reproduce the work or derivative works for the author's personal use or for the use of the company, provided that the source and the Informatika Mulawarman copyright notice are indicated, the copy is not used in any way implying the Journal of Informatika Mulawarman (JIM) approval of the product or service from any company, and the copy itself is not offered for sale.
- Although authors are permitted to reuse all or part of the Works in other works, this does not include granting third-party requests to reprint, republish, or other types of reuse.
Informatika Mulawarman by http://e-journals.unmul.ac.id/index.php/JIM/index is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
under the CC BY-SA license, authors and other users are able to reprint, distribute or use the material for commercial purposes so long as they give attribution to the journal Informatika Mulawarman and license the republished material under the same license.
