Detection of diseases using facial features with Deep transfer learning

D. Balakrishnan; Miriyala Sai Dhanunjai; Samudrala Naga Pranitha; Nara Haritha

doi:10.53730/ijhs.v6nS1.6843

Authors

D. Balakrishnan Assistant Professor, Department of Computer Science and engineering Kalasalingam Academy of Research & Education Krishnankovil, TamilNadu
Miriyala Sai Dhanunjai Student, Department of Computer Science and Engineering, Kalasalingam Academy of Research & Education, Krishnankovil, Tamilnadu
Samudrala Naga Pranitha Student, Department of computer Science and Engineering, Kalasalingam Academy of Research & Education, Krishnankovil, Tamilnadu
Nara Haritha Student, Department of Computer Science and Engineering, Kalasalingam Academy of Research & Education, Krishnankovil, Tamilnadu

Keywords:

Facial diagnosis, beta-thalassemia, hyperthyroidism, down syndrome, leprosy, vitiligo

Abstract

There is always a relation between face and diseases that leads the idea of facial diagnosis. The aim of this project is to predict the diseases using 2 Dimensional facial images. In this study, we are dealing with diagnosis of single and multiple illnesses such as Beta-thalassemia, hyperthyroidism, Down syndrome, leprosy and vitiligo. Accuracy for face recognition is over 90%. In practical collecting disease specific images are complex, expensive and time taking process. Therefore, collecting data sets for face recognition is hard and complicated. So, in deep transfer learning applications we can do facial diagnosis with a small dataset which will be easy process where we can train the pre trained model.

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References

Bo Jin, Leandro Cruz, N. Goncalves “Deep facial diagnosis: Deep transfer learning from face recognition to facial diagnosis” ieee explore, 2019.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in neural information processing systems, 2012, pp. 1097–1105.

C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 1–9.

K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556, 2014.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770–778.

F. Schroff, D. Kalenichenko, and J. Philbin, “Facenet: A unified embedding for face recognition and clustering,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 815– 823.

Y. Taigman, M. Yang, M. Ranzato, and L. Wolf, “Deepface: Closing the gap to human-level performance in face verification,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2014, pp. 1701–1708.

J. Liu, Y. Deng, T. Bai, Z. Wei, and C. Huang, “Targeting ultimate accuracy: Face recognition via deep embedding,” arXiv preprint arXiv:1506.07310, 2015.

B. Zhang, X. Wang, F. Karray, Z. Yang, and D. Zhang, “Computerized facial diagnosis using both color and texture features,” Information Sciences, vol. 221, pp. 49–59, 2013. [10] E. S. A. Alhaija, F. N. Hattab, and M. A. Al-Omari, “Cephalometric measurements and facial deformities in subjects with β-thalassaemia major,” The European Journal of Orthodontics, vol. 24, no. 1, pp. 9–19, 2002.

P. N. Taylor, D. Albrecht, A. Scholz, G. Gutierrez-Buey, J. H. Lazarus, C. M. Dayan, and O. E. Okosieme, “Global epidemiology of hyperthyroidism and hypothyroidism,” Nature Reviews Endocrinology, vol. 14, no. 5, p. 301, 2018.

Q. Zhao, K. Rosenbaum, R. Sze, D. Zand, M. Summar, and M. G. Linguraru, “Down syndrome detection from facial photographs using machine learning techniques,” in Medical Imaging 2013: Computer-Aided Diagnosis, vol. 8670. International Society for Optics and Photonics, 2013, p. 867003.

Q. Zhao, K. Okada, K. Rosenbaum, L. Kehoe, D. J. Zand, R. Sze, M. Summar, and M. G. Linguraru, “Digital facial dysmorphology for genetic screening: Hierarchical constrained local model using ica,” Medical image analysis, vol. 18, no. 5, pp. 699–710, 2014.

H. J. Schneider, R. P. Kosilek, M. Günther, J. Roemmler, G. K. Stalla, C. Sievers, M. Reincke, J. Schopohl, and R. P. Würtz, “A novel approach to the detection of acromegaly: accuracy of diagnosis by automatic face classification,” The Journal of Clinical Endocrinology & Metabolism, vol. 96, no. 7, pp. 2074–2080, 2011.

X. Kong, S. Gong, L. Su, N. Howard, and Y. Kong, “Automatic detection of acromegaly from facial photographs using machine learning methods,” EBioMedicine, vol. 27, pp. 94–102, 2018