FPGA based skin disease identification system using sift algorithm and K-NN

Joshua D. B. Mendoza; Noel B. Linsangan; Jumelyn L. Torres; Emmanuel Luis Villanueva

doi:10.1117/12.2572951

12 June 2020 FPGA based skin disease identification system using sift algorithm and K-NN

Joshua D. B. Mendoza, Noel B. Linsangan, Jumelyn L. Torres, Emmanuel Luis Villanueva

Proceedings Volume 11519, Twelfth International Conference on Digital Image Processing (ICDIP 2020); 115190R (2020) https://doi.org/10.1117/12.2572951
Event: Twelfth International Conference on Digital Image Processing, 2020, Osaka, Japan

Abstract

The initial assessment of any skin disease is usually made by visual inspection of doctors and skin specialists. Further tests may be recommended such as biopsy and pathological examination for a more accurate diagnosis. With the use of skin disease identification system, the diagnosis of infected skin is readily attainable without undergoing biopsy and pathological examination. The infected skin disease image is identified using SIFT algorithms with local features and KNN classifier. The skin disease that will be identified are acne, psoriasis, eczema, rashes, hives, warts, tinea versicolor (an-an) and unknown skin disease. The system was confirmed to be efficient in identifying the aforementioned skin diseases. Identification of infected skin images is accomplished by K-Nearest Neighbors (K-NN) algorithm which shows an accuracy of 90% in functionality testing.

Citation Download Citation

Joshua D. B. Mendoza, Noel B. Linsangan, Jumelyn L. Torres, and Emmanuel Luis Villanueva "FPGA based skin disease identification system using sift algorithm and K-NN", Proc. SPIE 11519, Twelfth International Conference on Digital Image Processing (ICDIP 2020), 115190R (12 June 2020); https://doi.org/10.1117/12.2572951

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