Finger Disability Recognition Based on Holistically-Nested Edge Detection

In order to relieve the medical pressure, when patients with finger disability see a doctor, the degree of finger disability can be identified and judged by the equipment first, and then the doctor carries out the next step of diagnosis and treatment. Aiming at the problem that the traditional recognition algorithm is not ideal, this paper proposes a finger disability recognition algorithm based on Holistically-nested edge detection algorithm. On the basis of extracting the edge of hand image with Holistically-nested edge detection algorithm, the similarity judgment is made between the experimental object’s hand edge detection image and the standard hand edge detection image. The degree of finger joint integrity was analyzed by different similarity judgment, and then the degree of finger disability was judged. In order to verify the effectiveness of the method, 50 people’s hand images were collected to establish a sample database of hand images, and a total of 600 simulated severed finger images were tested. The accuracy of finger disability recognition was 96.6%. This algorithm can effectively identify the degree of finger disability and improve the medical efficiency.