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.
This study highlights the importance of one of the most important components when prescription glasses are developed for a patient, which is the Pupillary Distance (PD). PD pertains to the distance between the two pupils. This is an essential information when having prescription glasses created to ensure that the pupils remain in the center of the glasses. This paper explores the application of the You Only Look Once (YOLO) algorithm in order to determine the distance between the two pupils measured. It has been found that the YOLO algorithm can be an efficient tool in measuring distances between the objects, which was proven by linear regression where the computed value and measured value are similar. The research has concluded that it can replicate the results from the conventional method of measuring Pupillary Distance through the use of a ruler.
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