title: ÐÐµÐ¹ÑÐ¾ÑÐµÑÐµÐ²ÑÐµ Ð¸ ÐºÐ»Ð°ÑÑÐ¸ÑÐµÑÐºÐ¸Ðµ Ð¼ÐµÑÐ¾Ð´Ñ Ð´Ð»Ñ Ð·Ð°Ð´Ð°ÑÐ¸ Ð°Ð²ÑÐ¾ÑÐ¾ÐºÑÑÐ° ÑÐ¸ÑÑÐ¾Ð²Ð¾Ð³Ð¾ Ð¼Ð¸ÐºÑÐ¾ÑÐºÐ¾Ð¿Ð°
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Neural network and classical methods for the task of autofocus of a digital microscope
Creators: name: ÐÐ½Ð¸ÑÐµÐ½ÐºÐ¾, ÐÐ¸ÑÐ°Ð¸Ð»
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2024
Subjects: Ð°Ð²ÑÐ¾ÑÐ¾ÐºÑÑÐ¸ÑÐ¾Ð²ÐºÐ°
ÑÐ¸ÑÑÐ¾Ð²Ð°Ñ Ð¼Ð¸ÐºÑÐ¾ÑÐºÐ¾Ð¿Ð¸Ñ
ÑÐ²ÑÑÑÐ¾ÑÐ½ÑÐµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
Ð°ÑÑ Ð¸ÑÐµÐºÑÑÑÐ° ÑÐµÑÐ¸
ÑÐ¾ÐºÐ°Ð»ÑÐ½Ð°Ñ Ð¿Ð»Ð¾ÑÐºÐ¾ÑÑÑ
autofocus
digital microscopy
convolutional neural networks
network architecture
focal plane

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description: This work is devoted to various methods of autofocus of digital microscopes, as well as the development and implementation of a new approach to this task using neural networks and deep learning. As a result, a digital microscope autofocus algorithm has been developed that does not depend on specific hardware and does not require integration into the microscope or camera software. The algorithm is based on the architecture of the convolutional neural network Mobile Net V3. The network was trained on an open source dataset consisting of microscope images taken at 20x magnification and integers indicating the distance to the focal plane, taking into account the direction. Based on the test results, the following conclusion was made: the presented algorithm can be actively applied in practice, since it has a high speed and excellent quality, not inferior to classical methods, as well as more resistant to noise and easy to implement.