Scale-Invariant Feature Extraction for Skin Image Detection
Keywords:
no keywordsAbstract
In many applications, including dermatology, biometrics, and medical diagnostics, skin image detection is essential. Because of the differences in lighting, positions, and scales, it is difficult to identify skin regions in images. The article presents a new method for scale-invariant feature extraction-based skin image detection. The proposed strategy makes use of scale-invariant features to improve the skin image detection's resilience at various scales. Scale-invariant feature transform (SIFT) is used to extract key points from skin images, enabling the identification of unique patterns regardless of their size. The skin portions in the image are then reliably represented by using these key points. The incorporation of machine learning methods to improve the skin image recognition procedure is also explored in this research. A model is trained on a broad dataset of skin photos to enable the system to learn and adapt to different skin kinds, circumstances, and image scales. The evaluation's findings show how well the suggested scale-invariant feature extraction technique works to recognize skin images with reliability and accuracy.
Downloads
References
Abdulrahman, B. F., Hawezi, R. S., MR, S. M. N., Kareem, S. W., & Ahmed, Z. R. (2022). Comparative Evaluation of Machine Learning Algorithms in Breast Cancer. Qalaai Zanist Journal, 7(1), 878-902.
Hotta, K., Kurita, T., & Mishima, T. (1998, April). Scale invariant face detection method using higher-order local autocorrelation features extracted from log-polar image. In Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition (pp. 70-75). IEEE..
Gutman, D., Codella, N. C., Celebi, E., Helba, B., Marchetti, M., Mishra, N., & Halpern, A. (2016). Skin lesion analysis toward melanoma detection: A challenge at the international symposium on biomedical imaging (ISBI) 2016, hosted by the international skin imaging collaboration (ISIC). arXiv preprint arXiv:1605.01397.
Azeem, A., Sharif, M., Shah, J. H., & Raza, M. (2015). Hexagonal scale invariant feature transform (H-SIFT) for facial feature extraction. Journal of applied research and technology, 13(3), 402-408.
Wang, Y., Li, Z., Wang, L., & Wang, M. (2013). A Scale Invariant Feature Transform Based Method. J. Inf. Hiding Multim. Signal Process., 4(2), 73-89.
Hussain, M., Zhao, J., Hassan, A., Rahman, H. U., Kashif, A. S., & Tang, Y. (2021, March). Application of Separate Modal Analysis and Scale-Invariant Feature Transform on Clinical Data for the Screening of Breast Cancer. In 2021 IEEE International Conference on Information Communication and Software Engineering (ICICSE) (pp. 36-40). IEEE.
Taha, M. A., Ahmed, H. M., & Husain, S. O. (2022). Iris Features Extraction and Recognition based on the Scale Invariant Feature Transform (SIFT). Webology, 19(1), 171-184.
Kavitha, J. C., Suruliandi, A., Nagarajan, D., & Nadu, T. (2017). Melanoma detection in dermoscopic images using global and local feature extraction. International Journal of Multimedia and Ubiquitous Engineering, 12(5), 19-28.
Wati, M., Puspitasari, N., Budiman, E., & Rahim, R. (2019, July). First-order feature extraction methods for image texture and melanoma skin cancer detection. In Journal of Physics: Conference Series (Vol. 1230, No. 1, p. 012013). IOP Publishing.
Selvia, A., Prakash, V. N., Saravanan, N., Jawahar, B., & Karthick, V. (2021). Skin lesion detection using feature extraction approach. Annals of the Romanian Society for Cell Biology, 3939-3951.
Mahmudi, I., Ahsan, A. C., Kasim, A. A., Nur, R., Basalamah, R., & Septiarini, A. (2020, October). Face Skin Disease Detection with Textural Feature Extraction. In 2020 6th International Conference on Science in Information Technology (ICSITech) (pp. 133-137). IEEE.
Zhi-fang, L., Zhi-sheng, Y., Jain, A. K., & Yun-qiong, W. (2003, September). Face detection and facial feature extraction in color image. In Proceedings Fifth International Conference on Computational Intelligence and Multimedia Applications. ICCIMA 2003 (pp. 126-130). IEEE.
Jana, E., Subban, R., & Saraswathi, S. (2017, December). Research on skin cancer cell detection using image processing. In 2017 IEEE International conference on computational intelligence and computing research (ICCIC) (pp. 1-8). IEEE..
Choi, Y. H., Tak, Y. S., Rho, S., & Hwang, E. (2013). Skin feature extraction and processing model for statistical skin age estimation. Multimedia tools and applications, 64, 227-247.
Takayama, K., Johan, H., & Nishita, T. (2012). Face detection and face recognition of cartoon characters using feature extraction. In Image, Electronics and Visual Computing Workshop (Vol. 48).
Saber, E., & Tekalp, A. M. (1998). Frontal-view face detection and facial feature extraction using color, shape and symmetry based cost functions. Pattern Recognition Letters, 19(8), 669-680..
Mikhail, D. Y., Hawezi, R. S., & Kareem, S. W. (2023). An Ensemble Transfer Learning Model for Detecting Stego Images. Applied Sciences, 13(12), 7021.
Ali, O. M. A., Kareem, S. W., & Mohammed, A. S. (2022). Comparative evaluation for two and five classes ECG signal classification: Applied deep learning. Journal of Algebraic Statistics, 13(3), 580-596.
Muhamad, H. A., Kareem, S. W., & Mohammed, A. S. (2022). A deep learning method for detecting leukemia in real images. NeuroQuantology, 20(7), 2358.
Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. In Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, 234-241.
Milletari, F., Navab, N., & Ahmadi, S.-A. (2016). V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation. In Proceedings of the 4th International Conference on 3D Vision, 3DV 2016, 565-571.
Oktay, O., Schlemper, J., le Folgoc, L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N. Y., & Kainz, B. (2018). Attention U-Net: Learning Where to Look for the Pancreas. arXiv:1804.03999.
Rafi, H., Rafiq, H., & Farhan, M. (2021). Inhibition of NMDA receptors by agmatine is followed by GABA/glutamate balance in benzodiazepine withdrawal syndrome. Beni-Suef University Journal of Basic and Applied Sciences, 10(1), 1-13.
Rafi, H., Ahmad, F., Anis, J., Khan, R., Rafiq, H., & Farhan, M. (2020). Comparative effectiveness of agmatine and choline treatment in rats with cognitive impairment induced by AlCl3 and forced swim stress. Current Clinical Pharmacology, 15(3), 251-264.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
