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AI HAM 10000 Database to Assist Residents in Learning Differential Diagnosis of Skin Cancer

This study uses the InceptionResNetV2 model provided by Google and the self-developed Convolutional Neural Network (CNN) algorithm to redefine the pixels and calculate the images in the HAM 10000 database built by Harvard University. The purpose is to help young dermatological residents learn the imaging features of skin cancer outside of office hours. In addition, when a patient comes to the clinic, the resident can immediately take a photo of the affected part and upload it to the artificial intelligence program of the IRV2 architecture. This helps to know the probability of certain skin diseases and compare it with the differential diagnosis knowledge learned from the visiting staff during the clinic, to rapidly accumulate experience in the differential diagnosis.