CONVOLUTIONAL NEURAL NETWORK FOR ANEMIA DETECTION BASED ON CONJUNCTIVA PALPEBRAL IMAGES
Abstract
Anemia is a condition in which the level of hemoglobin in a person's blood is below normal. Hemoglobin concentration is one of the parameters commonly used to determine a person's physical condition. Anemia can attack anyone, especially pregnant women. Currently, many non-invasive anemia detection methods have been developed. One of non-invasive methods in detecting anemia can be seen through its physiological characteristics, namely palpebral conjunctiva images. In this study, conjunctival image-based anemia detection was carried out using one of the deep learning methods, namely Convolutional Neural Netwok (CNN). This CNN method is used with the aim of obtaining more specific characteristics in distinguishing normal and anemic conditions based on the image of the palpebral conjunctiva. The Convolutional Neural Network proposed model in this study consists of five hidden layers, each of which uses a filter size of 3x3, 5x5, 7x7, 9x9, and 11x11 and output channels 16, 32, 64, 128 respectively. Fully connected layer and sigmoid activation function are used to classify normal and anemic conditions. The study was conducted using 2000 images of the palpebral conjunctiva which contained anemia and normal conditions. Furthermore, the dataset is divided into 1,440 images for training, 160 images for validation and 400 images for model testing. The study obtained the best accuracy of 94%, with the average value of precision, recall and f-1 score respectively 0.935; 0.94; 0.935. The results of the study indicate that the system is able to classify normal and anemic conditions with minimal errors. Furthermore, the system that has been designed can be implemented in an Android-based application so that the detection of anemia based on this palpebral conjunctival image can be carried out in real-tim.
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References
R. A. Rochmanto, H. Zakaria, R. D. Alviana, N. Shahib, “Non-invasive hemoglobin measurement for anemia diagnosis. International Conference on Electrical Engineering,” Computer Science and Informatics (EECSI), pp. 19–21, 2017. https://doi.org/10.1109/EECSI.2017.8239096
M. Y. Florestiyanto, and N. J. Peksi, “Non-Invasive Anemia Screening Using Nails and Palms Photos,” vol. 1, no. 1, pp. 311–318, 2020
D. Manisha, J. Prakash, Mishra, Anupam. “Image Segmentation of Eye for Non-Invasive Detection of Anemia,” 2018. https://ssrn.com/abstract=3282850
V. Agrawal, “Non-Invasive Anemia Detection in Pregnant Women,” vol. 3, no. 16, pp. 1–3, 2015.
Y. N. Fuadah, S. Sa'idah, I. Wijayanto, R. Patmasari and R. Magdalena, "Non Invasive Anemia Detection in Pregnant Women Based on Digital Image Processing and K-Nearest Neighbor," 2020 3rd International Conference on Biomedical Engineering (IBIOMED), pp. 60-64, 2020. doi: 10.1109/IBIOMED50285.2020.9487605.
S. Collings, O. Thompson, E. Hirst, L. Goossens, A. George, et al. “Non-Invasive Detection of Anaemia Using Digital Photographs of the Conjunctiva,” PLOS ONE, vol. 11, no. 4, 2016. doi: https://doi.org/10.1371/journal.pone.0153286
M. D. Anggraeni and A. Fatoni. “Non-invasive Self-Care Anemia Detection during Pregnancy Using a Smartphone Camera,” 2017 https://doi.org/10.1088/1742-6596/755/1/011001
P. V. Bhagat, and R. Singhal, “A Review Paper on Non-Invasive Methods for Determination of Anemia,” vol. 5, no. 2, pp. 693–694, 2018.
P. Jain, S. Bauskar, and M. Gyanchandani, “Neural network based non-invasive method to detect anemia from images of eye conjunctiva,” International Journal of Imaging Systems and Technology, vol. 30, no. 1, pp. 112–125, 2020. Doi: https://doi.org/10.1002/ima.22359
Y. N. Fu’adah, S. Saidah, I. Wijayanto, N. A. Ibrahim, S. Rizal, and R. Magdalena, “Computer Aided Diagnosis for Early Detection of Glaucoma Using Convolutional Neural Network (CNN),” 2021
K. K. Patro, A. Jaya Prakash, M. Jayamanmadha Rao, and P. Rajesh Kumar, “An Efficient Optimized Feature Selection with Machine Learning Approach for ECG Biometric Recognition,” IETE J. Res., vol. 0, no. 0, pp. 1–12, 2020.
JokerPT, “Binary Image classifier CNN using TensorFlow” 2020. https://medium.com/techiepedia/binary-image-classifier-cnn-using-tensorflow-a3f5d6746697 (accessed Mar. 4, 2022).
A. T. Putra, K. Usman, and S. Saidah, “Webinar Student Presence System Based on Regional Convolutional Neural Network Using Face Recognition,” Jurnal Teknik Informatika (Jutif), vol. 2, no. 2, pp. 109–118, 2021. Doi: https://doi.org/10.20884/1.jutif.2021.2.2.82
K. K. Patro, A. Jaya Prakash, M. Jayamanmadha Rao, and P. Rajesh Kumar, “An Efficient Optimized Feature Selection with Machine Learning Approach for ECG Biometric Recognition,” IETE J. Res., vol. 0, no. 0, pp. 1–12, 2020.
A. Hibatullah and I. Maliki, "Penerapan Metode Convolutional Neural Network Pada Pengenalan Pola Citra Sandi Rumput," Doctoral dissertation, Universitas Komputer Indonesia, 2019.
M. R. Alwanda, R. P. Kurniawan Ramadhan and D. Alamsyah, "Implementasi Metode Convolutional Neural Network Menggunakan Arsitektur LeNet-5 untuk Pengenalan Doodle," Jurnal Algoritme, vol. 1, no. 1, pp. 45-56, 2020.
M. N. Bajwa, M. I. Malik, S. A. Siddiqui, A. Dengel, F. Shafait, W. Neumeier and S. Ahmed, “Correction to: Two-stage framework for optic disc localization and glaucoma classification in retinal fundus images using deep learning,” BMC Medical Informatics and Decision Making, vol. 19, no. 136, pp. 1-16, 2019. DOI: 10.1186/s12911-019- 0842-8
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