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Artificial Neural Networks have been successfully applied to abroad spectrum of complex analysis problems. Computational intelligence is finding more and more applications in computer aided diagnostics, helping doctors to process large quantities of various medical data. In dermatology it is extremely difficult to perform automatic diagnostic differentiation of malignant melanoma based only on dermatoscopic images. Applying artificial intelligence algorithms to explore and search large database of dermatoscopic images allow doctors to semantically filter out image with specified characteristics. This paper presents an approach for characteristic objects classification found in image database of pigment skin lesions, based on radial basis function kernel for artificial neural networks.

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High tibial osteotomy correction angle calculation is a process that is usually performed manually or in a semi-automated way. The process, according to the Miniaci method, is divided into several stages to find specific points: the center of the femoral head, the edges of the tibial plateau, the Fujisawa point, the center of the ankle joint, and the Hinge point. In this paper, we proposed an end-to-end approach that consists of different techniques for finding each point. We used YOLOv4 to detect regions of interest. To identify the center of the femoral head, we used the YOLOv4 and the Hough transform. For the other points, we used a combined method of YOLOv4 with the ASM/AAM algorithm and YOLOv4 with image processing algorithms. Our fully-automated method achieved a mean error rate of 0.5◦ (0-2.76◦) ICC 0.99 (0.98-0.99) 95%CI on our own dataset of standing long-leg Anterior Posterior view X-rays. This might be the first method that automatically calculates the correction angle of high tibial osteotomy.