Abstract

Visual diagnosis of skin tumors is one of the most important steps in dealing with them. Nowadays, artificial intelligence has been booming and technology of automated cognition by computer has been improved. One of the bottlenecks in building an efficient cognition system is providing adequate amount of data to base on. The internet may be a mine of the data. Feasibility of using macroscopic skin tumor images on the internet for automated cognition was studied by a personal computer. Skin tumor images were collected with scraping software. The quality of images varied. The most time-consumed process was to select images visually. After the selection, 600 macroscopic images consisting of 5 categories were qualified. The images were plotted with cluster analysis algorithms. Unsupervised data clustering: k-means clustering, principal component analysis and t-distributed stochastic neighbor embedding could not cluster them in human comprehensible fashion. Three-dimensional plotting of a supervised data clustering: Linear discrimination analysis, showed relatively clear clustering. Convolution neural networks were trained and tested for categorical accuracy. Time, consumed for training networks on a personal computer, was satisfactory. Categorical accuracy depended on what network it employed. Fine-tuning of a pre-trained network scored best categorical accuracy. Augmenting training data statistically increased categorical accuracy. Despite variation of image quality, using skin tumor images on the internet is a feasible approach for automated cognition.

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