Abstract
Objectives
The main intention of this paper is to propose a new Improved K-means clustering algorithm, by optimally tuning the centroids.
Methods
This paper introduces a new melanoma detection model that includes three major phase’s viz. segmentation, feature extraction and detection. For segmentation, this paper introduces a new Improved K-means clustering algorithm, where the initial centroids are optimally tuned by a new algorithm termed Lion Algorithm with New Mating Process (LANM), which is an improved version of standard LA. Moreover, the optimal selection is based on the consideration of multi-objective including intensity diverse centroid, spatial map, and frequency of occurrence, respectively. The subsequent phase is feature extraction, where the proposed Local Vector Pattern (LVP) and Grey-Level Co-Occurrence Matrix (GLCM)-based features are extracted. Further, these extracted features are fed as input to Deep Convolution Neural Network (DCNN) for melanoma detection.
Results
Finally, the performance of the proposed model is evaluated over other conventional models by determining both the positive as well as negative measures. From the analysis, it is observed that for the normal skin image, the accuracy of the presented work is 0.86379, which is 47.83% and 0.245% better than the traditional works like Conventional K-means and PA-MSA, respectively.
Conclusions
From the overall analysis it can be observed that the proposed model is more robust in melanoma prediction, when compared over the state-of-art models.
-
Research funding: None declared.
-
Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
-
Competing interests: The authors declare that they have no conflict of interest.
-
Employment or leadership: None declared.
-
Ethical Approval: The conducted research is not related to either human or animal use.
References
1. Do, T, Hoang, T, Pomponiu, V, Zhou, Y, Chen, Z, Cheung, NM, et al.. Accessible melanoma detection using smartphones and mobile image analysis. IEEE Trans Multimed 2018;20:2849–64. https://doi.org/10.1109/tmm.2018.2814346.Search in Google Scholar
2. Hekler, A, Utikal, JS, Enk, AH, Hauschild, A, Collaborators. Superior skin cancer classification by the combination of human and artificial intelligence. Eur J Cancer 2019;120:114–21. https://doi.org/10.1016/j.ejca.2019.07.019.Search in Google Scholar PubMed
3. Risica, PM, Matthews, NH, Dionne, L, Mello, J, Weinstock, MA. Psychosocial consequences of skin cancer screening. Prev Med Rep 2018;10:310–6. https://doi.org/10.1016/j.pmedr.2018.04.011.Search in Google Scholar PubMed PubMed Central
4. Bhattacharjee, P, Das, A, Ashok, K, Pritha Bhattacharjee, G. Epigenetic regulations in alternative telomere lengthening: understanding the mechanistic insight in arsenic-induced skin cancer patients. Sci Total Environ 2020;704:135388. doi:https://doi.org/10.1016/j.scitotenv.2019.135388.Search in Google Scholar PubMed
5. Warsi, F, Khanam, R, Kamya, S, Paz Suárez-Araujo, C. An efficient 3D color-texture feature and neural network technique for melanoma detection. Inf Med Unlocked 2019;17:100176. doi:https://doi.org/10.1016/j.imu.2019.100176.Search in Google Scholar
6. Mirbeik-Sabzevari, A, Tavassolian, N. Ultrawideband, stable normal and cancer skin tissue phantoms for millimeter-wave skin cancer imaging. IEEE (Inst Electr Electron Eng) Trans Biomed Eng 2019;66:176–86. https://doi.org/10.1109/tbme.2018.2828311.Search in Google Scholar PubMed
7. Keshavarz, A, Vafapour, Z. Water-based terahertz metamaterial for skin cancer detection application. IEEE Sensor J 2019;19:1519–24. https://doi.org/10.1109/jsen.2018.2882363. Feb 15, 2019.Search in Google Scholar
8. Zhou, Y, Herman, C. Optimization of skin cooling by computational modeling for early thermographic detection of breast cancer. Int J Heat Mass Tran 2018;126:864–76. https://doi.org/10.1016/j.ijheatmasstransfer.2018.05.129.Search in Google Scholar
9. Yang, Y, Wu, R, Sargsyan, D, Yin, R, Kong, A-N. UVB drives different stages of epigenome alterations during progression of skin cancer. Canc Lett 2019;449:20–30. https://doi.org/10.1016/j.canlet.2019.02.010.Search in Google Scholar PubMed PubMed Central
10. Zhang, F, Jin, T, Hu, Q, He, P. Distinguishing skin cancer cells and normal cells using electrical impedance spectroscopy. J Electro Anal Chem 2018;823:531–6. https://doi.org/10.1016/j.jelechem.2018.06.021.Search in Google Scholar
11. Geetharamani, G, Aathmanesan, T. Split ring resonator inspired THz antenna for breast cancer detection. Optic Laser Technol 2020;126:106111. doi:https://doi.org/10.1016/j.optlastec.2020.106111.Search in Google Scholar
12. Dascalu, A, David, EO. Skin cancer detection by deep learning and sound analysis algorithms: a prospective clinical study of an elementary dermoscope. EBio Med 2019;43:107–13. https://doi.org/10.1016/j.ebiom.2019.04.055.Search in Google Scholar PubMed PubMed Central
13. Gordon, LG, Brynes, J, Baade, PD, Neale, RE, Janda, M. Cost-effectiveness analysis of a skin awareness intervention for early detection of skin cancer targeting men older than 50 years. Value Health 2017;20:593–601. https://doi.org/10.1016/j.jval.2016.12.017.Search in Google Scholar PubMed
14. Rahman, A, Rahman, AK, Rao, B. Early detection of skin cancer via terahertz spectral profiling and 3D imaging. Biosens Bioelectron 2016;82:64–70. https://doi.org/10.1016/j.bios.2016.03.051.Search in Google Scholar PubMed
15. Maron, RC, Weichenthal, M, Utikal, JS, Hekler, A, Collabrators. Systematic outperformance of 112 dermatologists in multiclass skin cancer image classification by convolutional neural networks. Eur J Cancer 2019;119:57–65. https://doi.org/10.1016/j.ejca.2019.06.013.Search in Google Scholar PubMed
16. Fusco, P, Cofini, V, Petrucci, E, Scimia, P, Paladini, G, Behr, AU, et al.. Unilateral paravertebral block compared with subarachnoid anesthesia for the management of postoperative pain syndrome after inguinal herniorrhaphy: a randomized controlled clinical trial. Pain 2016;157:1105–13. https://doi.org/10.1097/j.pain.0000000000000487.Search in Google Scholar PubMed
17. Bonacaro, A, Rubbi, I, Sookhoo, D. The use of wearable devices in preventing hospital readmission and in improving the quality of life of chronic patients in the homecare setting: a narrative literature review. Prof Inferm 2019;72:31550431.Search in Google Scholar
18. Massoudi, AH, Jameel, AS, Ahmad, AR. Stimulating organizational citizenship behavior by applying organizational commitment and satisfaction. Int J Soc Sci Econ Rev 2020;2:20–7. https://doi.org/10.36923/ijsser.v2i2.58.Search in Google Scholar
19. Zhou, Y, Herman, C. Optimization of skin cooling by computational modeling for early thermographic detection of breast cancer. Int J Heat Mass Tran 2018;126:864–76. https://doi.org/10.1016/j.ijheatmasstransfer.2018.05.129.Search in Google Scholar
20. Zhang, L, Ji, Z, Zhang, J, Yang, S. Photodynamic therapy enhances skin cancer chemotherapy effects through autophagy regulation. Photodiagnosis Photodyn Ther 2019;28:159–65. https://doi.org/10.1016/j.pdpdt.2019.08.023.Search in Google Scholar PubMed
21. Garcia, MR, Requena, MB, Pratavieira, S, Tan Moriyama, L, Magalhães, DV. Development of a system to treat and online monitor photodynamic therapy of skin cancer using PpIX near-infrared fluorescence. Photodiagnosis Photodyn Ther 2020;30:101680. doi:https://doi.org/10.1016/j.pdpdt.2020.101680.Search in Google Scholar PubMed
22. Vilanova Garcia, D, da Silva Filho, JI, Silveira, L, Tavares Pacheco, MT, Mario, MC. Analysis of Raman spectroscopy data with algorithms based on paraconsistent logic for characterization of skin cancer lesions. Vib Spectrosc 2019;103:102929. doi:https://doi.org/10.1016/j.vibspec.2019.102929.Search in Google Scholar
23. Ravi, RV, Subramaniam, K. Image compression and encryption using optimized wavelet filter bank and chaotic algorithm. Int J Appl Eng Res 2017;12:10595–610.Search in Google Scholar
24. Thangam, T, Kazem, HA, Muthuvel, K. SFOA: sun flower optimization algorithm to solve optimal power flow. Resbee Publishers.Search in Google Scholar
25. Rajakumar, BR. Static and adaptive mutation techniques for genetic algorithm: a systematic comparative analysis. Int J Comput Sci Eng 2013;8:180–93. https://doi.org/10.1504/IJCSE.2013.053087.Search in Google Scholar
26. Thomas, R, Rangachar, MJS. Hybrid optimization based DBN for face recognition using low-resolution images. Multimed Res 2018;1:33–43.Search in Google Scholar
27. Bossolasco, M, Maria Fenoglio, L. Yet another PECS usage: a continuous PECS block for anterior shoulder surgery. J Anaesthesiol Clin Pharmacol 2018;34:569. https://doi.org/10.4103/joacp.joacp_12_18.Search in Google Scholar PubMed PubMed Central
28. Manassero, A, Bossolasco, M, Ugues, S, Bailo, C. An atypical case of two instances of mepivacaine toxicity. J Anaesthesiol Clin Pharmacol 2014;30:582. https://doi.org/10.4103/0970-9185.142887.Search in Google Scholar PubMed PubMed Central
29. Nipanikar, SI, Hima Deepthi, V. Enhanced whale optimization algorithm and wavelet transform for image stenography. Multimed Res 2019;2:23–32.Search in Google Scholar
30. Vinolin, V. Breast cancer detection by optimal classification using GWO algorithm. Multimed Res 2019;2:10–8.Search in Google Scholar
31. Nida, N, Irtaza, A, Ali, J, Yousaf, MH, Mahmood, MT. Melanoma lesion detection and segmentation using deep region based convolutional neural network and fuzzy C-means clustering. Int J Med Inf 2019;124:37–48. https://doi.org/10.1016/j.ijmedinf.2019.01.005.Search in Google Scholar PubMed
32. Thanh, DNH, Surya Prasath, VB, Hieu, NN. Melanoma skin cancer detection method based on adaptive principal curvature, colour normalisation and feature extraction with the ABCD rule. J Digit Imag 2020;33:574–85. https://doi.org/10.1007/s10278-019-00316-x.Search in Google Scholar PubMed PubMed Central
33. Tan, TY, Zhang, L, Lim, CP. Intelligent skin cancer diagnosis using improved particle swarm optimization and deep learning models. Appl Soft Comput 2019;84:105725. https://doi.org/10.1016/j.asoc.2019.105725.Search in Google Scholar
34. Mirbeik-Sabzevari, A, Li, S, Garay, E, Nguyen, H, Wang, H, Tavassolian, N. Synthetic ultra-high-resolution millimeter-wave imaging for skin cancer detection. IEEE (Inst Electr Electron Eng) Trans Biomed Eng 2019;66:61–71. https://doi.org/10.1109/tbme.2018.2837102.Search in Google Scholar
35. Tan, TY, Zhang, L, Chin Neoh, S, Lim, CP. Intelligent skin cancer detection using enhanced particle swarm optimization. Knowl Base Syst 2018;158:118–35. https://doi.org/10.1016/j.knosys.2018.05.042.Search in Google Scholar
36. Zhang, N, Cai, Y-X, Wang, Y-Y, Tian, Y-T, Badami, B. Skin cancer diagnosis based on optimized convolutional neural network. Artif Intell Med 2020;102:101756. https://doi.org/10.1016/j.artmed.2019.101756.Search in Google Scholar PubMed
37. Jaworek-Korjakowska, J, Kłeczek, P. Automatic classification of specific melanocytic lesions using artificial intelligence. BioMed Res Int 2016. https://doi.org/10.1155/2016/8934242.10.1155/2016/8934242Search in Google Scholar PubMed PubMed Central
38. Goyal, M, Oakley, A, Bansal, P, Dancey, D, Yap, MH. Skin lesion segmentation in dermoscopic images with ensemble deep learning methods. IEEE Access 2019;8:4171–81. https://doi.org/10.1109/ACCESS.2019.2960504.Search in Google Scholar
39. Anton, N, Arponen, O, Aki, N, Masarwah, A, Anna, S, Liimatainen, T, et al.. Quantitative volumetric K-means cluster segmentation of fibroglandular tissue and skin in breast MRI. J Digit Imag 2018;31:425–34. https://doi.org/10.1007/s10278-017-0031-1.Search in Google Scholar PubMed PubMed Central
40. Boothalingam, R. Optimization using lion algorithm: a biological inspiration from lion’s social behavior. Evol Intell 2018;11:31–52. https://doi.org/10.1007/s12065-018-0168-y.Search in Google Scholar
41. Rajakumar, BR. Optimization using lion algorithm: a biological inspiration from lion’s social behavior. Evol Intell, Special Issue on Nature inspired algorithms for high performance computing in computer vision 2018;11:31–52. https://doi.org/10.1007/s12065-018-0168-y.Search in Google Scholar
42. Rajakumar, BR. Lion algorithm for standard and large scale bilinear system identification: a global optimization based on Lion’s social behavior. Beijing, China: IEEE Congress on Evolutionary Computation; 2014:2116–23 pp.10.1109/CEC.2014.6900561Search in Google Scholar
43. Rajakumar, BR. The Lion’s algorithm: a new nature inspired search algorithm. In Procedia Technology-2nd International Conference on Communication, Computing & Security, vol 6; 2012:126–35 pp. https://doi.org/10.1016/j.protcy.2012.10.016 (Elsevier).Search in Google Scholar
44. Rajakumar, BR. Lion algorithm and its applications. In: Khosravy, M, Gupta, N, Patel, N, Senju, T, editors. Frontier applications of nature inspired computation in Springer tracts in nature-inspired computing (STNIC). Springer; 2020.10.1007/978-981-15-2133-1_5Search in Google Scholar
45. Fan, K-C, Hung, T-Y. A novel local pattern descriptor—local vector pattern in high-order derivative space for face recognition. IEEE Trans Image Proc 2014;23:2877–91. doi:https://doi.org/10.1109/tip.2014.2321495.Search in Google Scholar PubMed
46. Properties of variance. Available from: https://en.wikipedia.org/wiki/Qualitative_variation [Accessed 13 05 2020].Search in Google Scholar
47. Arabi, PM, Joshi, G, Deepa, NV. Performance evaluation of GLCM and pixel intensity matrix for skin texture analysis. Perspect Sci 2016;8:203–6. https://doi.org/10.1016/j.pisc.2016.03.018.Search in Google Scholar
48. Gu, J, Wang, Z, Kuen, J, Ma, L, Shahroudy, A, Shuai, B, et al.. Recent advances in convolutional neural networks. Pattern Recogn 2018;77:354–77. https://doi.org/10.1016/j.patcog.2017.10.013.Search in Google Scholar
49. Mukherjee, S, Adhikari, A, Roy, M. Malignant melanoma detection using multilayer perceptron with optimized network parameter selection by PSO. In Contemporary advances in innovative and applicable information technology; 2018;812:101–9. https://doi.org/10.1007/978-981-13-1540-4_11.Search in Google Scholar
50. Sukanya. Deep learning based melanoma detection with optimized features via hybrid algorithm. In Communication; 2019.Search in Google Scholar
51. Jadhav, AR, Ghontale, AG, Shrivastava, VK. Segmentation and border detection of melanoma lesions using convolutional neural network and SVM. In Computational Intelligence: Theories, Applications and Future Directions; 2018, vol 1:97–108 pp. https://doi.org/10.1007/978-981-13-1132-1_8.Search in Google Scholar
© 2020 Walter de Gruyter GmbH, Berlin/Boston
