Current Science

Open Access Open Access  Restricted Access Subscription Access

Non-Invasive Method of Melanoma Detection on the Skin Surface through Extraction of Image Features Using Modified CAT Optimization Algorithm


Affiliations
1 Department of Electronics and Communication, Aalim Muhammed Salegh College of Engineering, I.A.F., Avadi, Chennai 600 055, India
 

In this study, melanoma was detected at an early stage using modified CAT optimization algorithm (MCOA) based on non-convex boundary edge extraction, pixel size, shape and intensity variations on the skin. MCOA can detect skin cancer at an early stage by extracting the non-convex border of the affected region prevent cancer spread. Thus melanoma is curable when detected at an early stage. MCOA extracts image features and obtains non-convex boundaries of melanoma in the skin image. The non-convex boundary region leads to visualization of discriminative features of melanoma based on the region of interest and scaling. The proposed MCOA delineates the affected region through non-convex border extraction and edge detection. An accuracy of 85% was obtained in the detection of melanoma using MCOA, when compared to traditional algorithms.

Keywords

Contour Refinement, Edge Detection, Melanoma, Non-Convex Boundary, Optimization Algorithm.
User
Notifications
Font Size

  • Siegel, R. L., Miller, K. D., Fuchs, H. E. and Jemal, A., Cancer statistics. CA: Cancer J. Clin., 2021, 71, 7–33; doi:10.3322/caac.21654.

  • Petrie, T., Samatham, R., Witkowski, A. M., Esteva, A. and Leachman, S. A., Melanoma early detection: big data, bigger picture. J. Invest. Dermatol., 2019, 139(1), 25–30; doi:10.1016/j.jid.2018.06.187.

  • Narayanamurthy, V. et al., Skin cancer detection using non-invasive techniques. RSC Adv., 2018, 49, 28095–28130; doi:10.1039/c8ra0-4164d.

  • Olugbara, O. O., Taiwo, T. B. and Heukelman, D., Segmentation of melanoma skin lesion using perceptual color difference saliency with morphological analysis. Math. Probl. Eng., 2018; doi:10.1155/2018/1524286.

  • Masood, A. and Al-Jumaily, A. A., Computer-aided diagnostic support system for skin cancer: a review of techniques and algorithms. Int. J. Biomed. Imag., 2013; doi:10.1155/2013/323268.

  • Dey, N., Rajinikanth, V., Ashour, A. S. and Tavares, J. M. R. S., Social group optimization supported segmentation and evaluation of skin melanoma images. Symmetry, 2018; doi:10.3390/sym100-20051.

  • Saba, T., Recent advancement in cancer detection using machine learning: systematic survey of decades, comparisons and challenges. J. Infect. Public Health, 2020, 13, 1274–1289; doi:10.1016/j.jiph.2020.06.033.

  • Rout, R., Parida, P., Alotaibi, Y., Alghamdi, S. and Khalaf, O. I., Skin lesion extraction using multiscale morphological local variance reconstruction based watershed transform and fast fuzzy c-means clustering. Symmetry, 2021, 13(11), 2085; doi:10.3390/sym13112085.

  • Javed, R., Rahim, M. S. M., Saba, T. and Rehman, A., A comparative study of features selection for skin lesion detection from dermoscopic images. Netw. Model. Anal. Health Informat., Bioinformat., 2020, 9; doi:10.1007/s13721-019-0209-1.

  • Pereira, P. M. M. et al., Dermoscopic skin lesion image segmentation based on local binary pattern clustering: comparative study. Biomed. Signal Process. Control, 2020, 59, 101924; doi:10.1016/j.bspc.2020.101924.

  • Adegun, A. and Viriri, S., FCN-based DenseNet framework for automated detection and classification of skin lesions in dermoscopy images. IEEE Access, 2020, 8, 150377–150396; doi:10.1109/ACCESS.2020.3016651.

  • Albahli, S., Nida, N., Irtaza, A., Yousaf, M. H. and Mahmood, M. T., Melanoma lesion detection and segmentation using YOLOv4-DarkNet and active contour. IEEE Access, 2020, 8, 198403–198414; doi:10.1109/access.2020.3035345.

  • Ali, A. R. H., Li, J. and Yang, G., Automating the ABCD rule for melanoma detection: a survey. IEEE Access, 2020, 8, 83333–83346; doi:10.1109/ACCESS.2020.2991034.

  • Arab, H., Chioukh, L., Dashti Ardakani, M., Dufour, S. and Tatu, S. O., Early-stage detection of melanoma skin cancer using contactless millimeter-wave sensors. IEEE Sens. J., 2020, 20(13), 7310–7317; doi:10.1109/JSEN.2020.2969414.

  • Ashraf, R. et al., Region-of-interest based transfer learning assisted framework for skin cancer detection. IEEE Access, 2020, 8, 147858–147871; doi:10.1109/ACCESS.2020.3014701.

  • Gan, K. B., Chong, K. S., Nawoor, A. D., Then, S. M., Abdul Murad, N. A. and Jamal, A. R. A., Development of an HLA-B*58:01 allele screening system for allopurinol-induced severe cutaneous adverse reactions detection. IEEE Access, 2020, 8, 225306–225323; doi:10.1109/ACCESS.2020.3044562.

  • Goyal, M., Oakley, A., Bansal, P., Dancey, D. and Yap, M. H., Skin lesion segmentation in dermoscopic images with ensemble deep learning methods. IEEE Access, 2020, 8, 4171–4181; doi:10.1109/ACCESS.2019.2960504.

  • Gu, Y., Ge, Z., Bonnington, C. P. and Zhou, J., Progressive transfer learning and adversarial domain adaptation for cross-domain skin disease classification. IEEE J. Biomed. Health Informat., 2020, 24(5), 1379–1393; doi:10.1109/JBHI.2019.2942429.

  • Ichim, L. and Popescu, D., Melanoma detection using an objective system based on multiple connected neural networks. IEEE Access, 2020, 8, 179189–179202; doi:10.1109/access.2020.3028248.

  • Kelman, Y. T., Yitzhak, H. L., Shabairou, N., Finder, S. and Zalevsky, Z., Multi-spectral optimization for tissue probing using machine learning. IEEE Photon. J., 2021, 13(1); doi:10.1109/JPHOT.2020.3048015.

  • Mansutti, G., Mobashsher, A. T., Bialkowski, K., Mohammed, B. and Abbosh, A., Millimeter-wave substrate integrated waveguide probe for skin cancer detection. IEEE Trans. Biomed. Eng., 2020, 67(9), 2462–2472; doi:10.1109/TBME.2019.2963104.

  • Naeem, A., Farooq, M. S., Khelifi, A. and Abid, A., Malignant melanoma classification using deep learning: datasets, performance measurements, challenges and opportunities. IEEE Access, 2020, 8, 110575–110597; doi:10.1109/ACCESS.2020.3001507.

  • Pham, T. C., Doucet, A., Luong, C. M., Tran, C. T. and Hoang, V. D., Improving skin-disease classification based on customized loss function combined with balanced mini-batch logic and real-time image augmentation. IEEE Access, 2020, 8, 150725–150737; doi:10.1109/ACCESS.2020.3016653.

  • Song, L., Lin, J., Wang, Z. J. and Wang, H., An end-to-end multi-task deep learning framework for skin lesion analysis. IEEE J. Biomed. Health Informat., 2020, 24, 2912–2921; doi:10.1109/JBHI.2020.2973614.

  • Talavera-Martinez, L., Bibiloni, P. and Gonzalez-Hidalgo, M., Hair segmentation and removal in dermoscopic images using deep learning. IEEE Access, 2021, 9, 2694–2704; doi:10.1109/ACCESS.2020.3047258.


Abstract Views: 281

PDF Views: 129




  • Non-Invasive Method of Melanoma Detection on the Skin Surface through Extraction of Image Features Using Modified CAT Optimization Algorithm

Abstract Views: 281  |  PDF Views: 129

Authors

N. Prabhakaran
Department of Electronics and Communication, Aalim Muhammed Salegh College of Engineering, I.A.F., Avadi, Chennai 600 055, India

Abstract


In this study, melanoma was detected at an early stage using modified CAT optimization algorithm (MCOA) based on non-convex boundary edge extraction, pixel size, shape and intensity variations on the skin. MCOA can detect skin cancer at an early stage by extracting the non-convex border of the affected region prevent cancer spread. Thus melanoma is curable when detected at an early stage. MCOA extracts image features and obtains non-convex boundaries of melanoma in the skin image. The non-convex boundary region leads to visualization of discriminative features of melanoma based on the region of interest and scaling. The proposed MCOA delineates the affected region through non-convex border extraction and edge detection. An accuracy of 85% was obtained in the detection of melanoma using MCOA, when compared to traditional algorithms.

Keywords


Contour Refinement, Edge Detection, Melanoma, Non-Convex Boundary, Optimization Algorithm.

References





DOI: https://doi.org/10.18520/cs%2Fv124%2Fi5%2F562-569