98
S. Zorgui et al.
5 Conclusion
In this paper, we proposed a new method to classify RCM images into healthy
and lentigo skins. This method is based on the InceptionV3 CNN architecture.
The network was trained with a dataset of 374 images and tested on 54 images
of different stacks and depths. The suggested CNN method shows huge potential
and very promising results. In future work, we will focus on applying the proposed approach to larger datasets and comparisons to other deep architectures.
Acknowledgements. The authors would like to thank Gwendal JOSSE et Jimmy Le
Digabel from Lab. Pierre Fabre for providing data.
References
1. Rajadhyaksha, M., Grossman, M., Esterowitz, D., Webb, R.H., Anderson, R.R.:
In vivo confocal scanning laser microscopy of human skin: melanin provides strong
contrast. J. Invest. Dermatol. 104, 946–952 (1995)
2. Laruelo, A., et al.: Hybrid sparse regularization for magnetic resonance spectroscopy. In: IEEE International Conference of Engineering in Medicine and Biology Society (EMBC), Osaka, Japan, 3–7 July 2013, pp. 6768–6771 (2013)
3. Albughdadi, M., Chaari, L., Tourneret, J.Y., Forbes, F., Ciuciu, P.: A Bayesian
non-parametric hidden Markov random model for hemodynamic brain parcellation.
Sig. Process. 135(10223), 132–146 (2017)
4. Chaabene, S., Chaari, L., Kallel, A.: Bayesian sparse regularization for parallel
MRI reconstruction using complex Bernoulli–Laplace mixture priors. SIViP 14(3),
445–453 (2019). https://doi.org/10.1007/s11760-019-01567-5
5. Fakhfakh, M., Bouaziz, B., Gargouri, F., Chaari, L.: 1ProgNet: Covid-19 prognosis
using recurrent and convolutional neural networks. IEEE Trans. Artif. Intell. (2020,
submitted)
6. Geert, L., et al.: A survey on deep learning in medical image analysis. Med. Image
Anal. 42, 60–88 (2017)
7. Calzavara-Pinton, P., Longo, C., Venturini, M., Sala, R., Pellacani, G.: Reflectance
confocal microscopy for in vivo skin imaging. Photochem. Photobiol. 84, 1421–1430
(2008)
8. Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights Imaging 9(4), 611–629
(2018). https://doi.org/10.1007/s13244-018-0639-9
9. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. Computing Research Repository (CoRR)
(2015)
10. Halimi, A., Batatia, H., Digabel, J., Josse, G., Tourneret, J.Y.: Statistical modeling
and classification of reflectance confocal microscopy images. In: Computational
Advances in Multi-Sensor Adaptive Processing (CAMSAP), pp. 1–5, December
2017
11. Halimi, A., Batatia, H., Digabel, J., Josse, G., Tourneret, J.Y.: An unsupervised
Bayesian approach for the joint reconstruction and classification of cutaneous
reflectance confocal microscopy images. In: European Signal Processing Conference EUSIPCO, pp. 241–245, August 2017
S. Zorgui et al.
5 Conclusion
In this paper, we proposed a new method to classify RCM images into healthy
and lentigo skins. This method is based on the InceptionV3 CNN architecture.
The network was trained with a dataset of 374 images and tested on 54 images
of different stacks and depths. The suggested CNN method shows huge potential
and very promising results. In future work, we will focus on applying the proposed approach to larger datasets and comparisons to other deep architectures.
Acknowledgements. The authors would like to thank Gwendal JOSSE et Jimmy Le
Digabel from Lab. Pierre Fabre for providing data.
References
1. Rajadhyaksha, M., Grossman, M., Esterowitz, D., Webb, R.H., Anderson, R.R.:
In vivo confocal scanning laser microscopy of human skin: melanin provides strong
contrast. J. Invest. Dermatol. 104, 946–952 (1995)
2. Laruelo, A., et al.: Hybrid sparse regularization for magnetic resonance spectroscopy. In: IEEE International Conference of Engineering in Medicine and Biology Society (EMBC), Osaka, Japan, 3–7 July 2013, pp. 6768–6771 (2013)
3. Albughdadi, M., Chaari, L., Tourneret, J.Y., Forbes, F., Ciuciu, P.: A Bayesian
non-parametric hidden Markov random model for hemodynamic brain parcellation.
Sig. Process. 135(10223), 132–146 (2017)
4. Chaabene, S., Chaari, L., Kallel, A.: Bayesian sparse regularization for parallel
MRI reconstruction using complex Bernoulli–Laplace mixture priors. SIViP 14(3),
445–453 (2019). https://doi.org/10.1007/s11760-019-01567-5
5. Fakhfakh, M., Bouaziz, B., Gargouri, F., Chaari, L.: 1ProgNet: Covid-19 prognosis
using recurrent and convolutional neural networks. IEEE Trans. Artif. Intell. (2020,
submitted)
6. Geert, L., et al.: A survey on deep learning in medical image analysis. Med. Image
Anal. 42, 60–88 (2017)
7. Calzavara-Pinton, P., Longo, C., Venturini, M., Sala, R., Pellacani, G.: Reflectance
confocal microscopy for in vivo skin imaging. Photochem. Photobiol. 84, 1421–1430
(2008)
8. Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights Imaging 9(4), 611–629
(2018). https://doi.org/10.1007/s13244-018-0639-9
9. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. Computing Research Repository (CoRR)
(2015)
10. Halimi, A., Batatia, H., Digabel, J., Josse, G., Tourneret, J.Y.: Statistical modeling
and classification of reflectance confocal microscopy images. In: Computational
Advances in Multi-Sensor Adaptive Processing (CAMSAP), pp. 1–5, December
2017
11. Halimi, A., Batatia, H., Digabel, J., Josse, G., Tourneret, J.Y.: An unsupervised
Bayesian approach for the joint reconstruction and classification of cutaneous
reflectance confocal microscopy images. In: European Signal Processing Conference EUSIPCO, pp. 241–245, August 2017