Chimp optimization algorithm to optimize a convolution neural network for skin detection in HVS and RGB images

Document Type : Research Paper


Department of Computer, Payame Noor University, PO. BOX 19395-3697 Tehran, Iran


Skin detection is a useful and popular method to identify and recognize human body parts, faces, naked people, and skin diseases and retrieve people in multimedia databases. Therefore, finding a suitable method to divide the pixels of an image into different skin groups can be very important. One of the most common methods is based on convolutional neural networks (CNN). However, the process of training a CNN is a challenging issue. Various optimization strategies have recently been used to optimize CNN biases and weights, such as the firefly algorithm (FA) and ant colony optimization (ACO). In this study, we use a well-known nature-inspired technique called Chimp optimization algorithm (ChOA) to train a classical LeNet-5 CNN structure for skin detection. The proposed skin classification algorithms operate directly on the RGB and HVS color space. The results clearly show that the proposed algorithm significantly improves the performance of a convolutional neural network.


[1] J. Ahmad, K. Muhammad, S. Bakshi, and S.W. Baik, Object-oriented convolutional features for fine-grained image retrieval in large surveillance datasets, Future Gener. Comput. Syst. 81 (2018), 314–330.
[2] Z. Al-Tairi, R. Wirza, M.I. Saripan, and P. Sulaiman, Skin segmentation using YUV and RGB color spaces, J. Inf. Process. Syst. 10 (2014), 283–299.
[3] J.P.B. Casati, D.R. Moraes, and E.L.L. Rodrigues, SFA: A human skin image database based on FERET and AR facial images, Anais, Rio de Janeiro: Escola de Engenharia de Sao Carlos, Universidade de S˜ao Paulo, 2013. Disponıvel em: WVC2013/Poster/2/3.pdf. Acesso em: 27 maio 2023.
[4] H. Ghayoumi Zadeh, A. Fayazi, O. Rahmani Seryasat, and H. Rabiee, A bidirectional long short-term neural network model to predict air pollutant concentrations: A case study of Tehran, Iran, Trans. Machine Intell. 5 (2022), no. 2, 63–76.
[5] J. Haddadnia, O.R. Seryasat, and H. Rabiee, Thyroid diseases diagnosis using probabilistic neural network and principal component analysis, J. Basic Appl. Sci. Res. 3 (2013), no. 2, 593–598.
[6] J. Hartung, A. Jacquin, J. Pawlyk, J. Rosenberg, H. Okada, and P.E. Crouch, Object-oriented H. 263 compatible video coding platform for conferencing applications, IEEE J. Selected Areas Commun. 16 (1998), no. 1, 42–55.
[7] T.J. Hong, S.V. Bhandary, S. Sobha, H. Yuki, B. Akanksha, U. Raghavendra, A.K. Rao, B. Raju, N.S. Shetty, A. Gertych, and K.C. Chua, Age-related macular degeneration detection using deep convolutional neural network, Future Gener. Comput. Syst. 87 (2018), 127–135.
[8] M.A. Islam, D.T. Anderson, A.J. Pinar, T.C. Havens, G. Scott, and J.M. Keller, Enabling explainable fusion in deep learning with fuzzy integral neural networks, IEEE Trans. Fuzzy Syst. 28 (2019), no. 7, 1291–1300.
[9] M. Khishe and M.R. Mosavi, Chimp optimization algorithm, Expert Syst. Appl. 149 (2020), 113338.
[10] M. Khishe and M.R. Mosavi, Classification of underwater acoustical dataset using neural network trained by chimp optimization algorithm, Appl. Acoustics 157 (2020), 107005.
[11] S. Khosravi and A. Chalechale, Chimp optimization algorithm to optimize a convolutional neural network for recognizing Persian/Arabic handwritten words, Math. Prob. Engin. 2022 (2022).
[12] C.L. Kumari and V.K. Kamboj, An effective solution to single-area dynamic dispatch using improved chimp optimizer, In E3S Web of Conferences, EDP Sci. 184 (2020), 01069.
[13] Y. LeCun, Y. Bengio, and G. Hinton, Deep learning, Nature 521 (2015), no. 7553, 436–444.
[14] H. Luo, A. Eleftheriadis, and J. Kouloheris, Statistical model-based video segmentation and its application to very low bit-rate video coding, Signal Process.: Image Commun. 16 (2000), no. 3, 333–352.
[15] R. Mudassar, S. Muhammad, Y. Mussarat, K.M. Attique, S. Tanzila, and F.S. Lawrence, Appearance-based pedestrians’ gender recognition by employing stacked autoencoders in deep learning, Future Gener. Comput. Syst. 88 (2018), 28–39.
[16] K. Nikolskaia, N. Ezhova, A. Sinkov, and M. Medvedev, Skin detection technique based on HSV color model and SLIC segmentation method, Proc. 4th Ural Workshop on Parallel, Distributed, and Cloud Computing for Young Scientists, Ural-PDC 2018, CEUR Workshop Proc. 2281 (2018), 123–135.
[17] O. Rahmani-Seryasat, J. Haddadnia and H. Ghayoumi-Zadeh, A new method to classify breast cancer tumors and their fractionation, Ciˆencia Natura 37 (2015), no. 4, 51–57.
[18] N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, Dropout: A simple way to prevent neural networks from overfitting, J. Mach. Learn. Res. 15 (2014), 1929–1958.
[19] M. Zafar, M.I. Sharif, M.I. Sharif, S. Kadry, S.A.C. Bukhari, and H.T. Rauf, Skin lesion analysis and cancer detection based on machine/deep learning techniques: A comprehensive survey, Life 13 (2023), no. 1, 146.
Volume 15, Issue 4
April 2024
Pages 275-284
  • Receive Date: 15 February 2023
  • Revise Date: 19 May 2023
  • Accept Date: 23 May 2023