@inproceedings{1f21a441f9f3490d804ea856cbaf391a,
title = "Transformer Based Semantic Segmentation Network for Medical Imaging Application",
abstract = "Deep learning plays a vital role in revolutionizing the health-care system, primarily in disease diagnosis, enabling the automatic segmentation of clinical images. The manual process of analysis is a tedious and time-consuming task even for experts, which may lead to imprecise evaluation. In this paper, a Transformer Based Semantic Segmentation Network is proposed as a new method for applications in the area of medical imaging. The novelty approach outperforms the majority of state-of-the art models achieving 98.45\% of accuracy. It was tested with the {\textquotedblleft}Bacteria detection with dark-field microscopy{\textquotedblright} dataset, which consists of 366 images of spirochaete bacteria mixed with red blood cells.",
keywords = "Deep Learning, Neural Network, Semantic Segmentation Neural Network, Transformer",
author = "Micha̷l Wieczorek and Jakub Si̷lka and Katarzyna Wiltos and Marcin Wo{\'z}niak",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.; 23rd International Conference on Artificial Intelligence and Soft Computing, ICAISC 2024 ; Conference date: 16-06-2024 Through 20-06-2024",
year = "2025",
doi = "10.1007/978-3-031-84356-3\_31",
language = "English",
isbn = "9783031843556",
series = "Lecture Notes in Computer Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "380--389",
editor = "Leszek Rutkowski and Marcin Korytkowski and Rafal Scherer and Ryszard Tadeusiewicz and Witold Pedrycz and Zurada, \{Jacek M.\}",
booktitle = "Artificial Intelligence and Soft Computing - 23rd International Conference, ICAISC 2024, Proceedings",
address = "Germany",
}