Dhanashree, K., Jayabal, P., Kumar, A., Logeswari, S., Priya, K.. (1400). Fingernail analysis for early detection and diagnosis of diseases using machine learning techniques. هنرهای کاربردی, 13(Special Issue for selected papers of ICDACT-2021), 61-69. doi: 10.22075/ijnaa.2022.6331
K. Dhanashree; P. Jayabal; A. Saran Kumar; S. Logeswari; K. Ranjeetha Priya. "Fingernail analysis for early detection and diagnosis of diseases using machine learning techniques". هنرهای کاربردی, 13, Special Issue for selected papers of ICDACT-2021, 1400, 61-69. doi: 10.22075/ijnaa.2022.6331
Dhanashree, K., Jayabal, P., Kumar, A., Logeswari, S., Priya, K.. (1400). 'Fingernail analysis for early detection and diagnosis of diseases using machine learning techniques', هنرهای کاربردی, 13(Special Issue for selected papers of ICDACT-2021), pp. 61-69. doi: 10.22075/ijnaa.2022.6331
Dhanashree, K., Jayabal, P., Kumar, A., Logeswari, S., Priya, K.. Fingernail analysis for early detection and diagnosis of diseases using machine learning techniques. هنرهای کاربردی, 1400; 13(Special Issue for selected papers of ICDACT-2021): 61-69. doi: 10.22075/ijnaa.2022.6331

Fingernail analysis for early detection and diagnosis of diseases using machine learning techniques

International Journal of Nonlinear Analysis and Applications
دوره 13، Special Issue for selected papers of ICDACT-2021، خرداد 2022، صفحه 61-69    اصل مقاله (793.92 K)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22075/ijnaa.2022.6331
نویسندگان
K. Dhanashree* 1؛ P. Jayabal2؛ A. Saran Kumar3؛ S. Logeswari3؛ K. Ranjeetha Priya4
1Department of CSE, Sri Ramakrishna Engineering College, Coimbatore, Tamil Nadu, India
2Department of Mathematics, Rathinam Technical Campus, Coimbatore, Tamil Nadu, India
3Department of CSE, Bannari Amman Institute of Technology, Erode, Tamil Nadu, India
4Department of Information Technology,Sri Krishna College of Engineering and Technology, Coimbatore, Tamil Nadu, India
چکیده
Each and every human have unique fingernails. In the early days, the psychological conditions of the human body were reflected with the help of the growth situation of the surface of nails.  It is possible to diagnose human nails and predict the disease. Predicting the disease at the early stage helps in preventing the disease. In this proposed work, the image of the nail is taken from a microscopic image. The lunula and nail plate are segmented effectively using the image pre-processing techniques. Histograms of oriented gradients and local binary patterns are used to capture the characteristic value. Once after pre-processing various features of the nails are extracted using various machine learning algorithms such as Support Vector Machines, Multiclass Support Vector Machine, Convolution Neural Network along with an Optimization algorithm named Ant Colony Optimization to improve the efficiency of classification.
کلیدواژه‌ها
Local Binary Pattern (LBP)؛ Block Chain Technology (BCT)؛ Machine Learning (ML)
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