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A Transformer-based Method for Skin Fungi Identification from Fluorescent Images

Published: 28 September 2023 Publication History

Abstract

Due to the increasing number of patients with impaired immune function and the widespread use of broad-spectrum antibiotics, the rate of fungal infections is rising and invasive fungal infections (IFD) have become one of the major causes of death. Therefore, early diagnosis and treatment of IFD is the focus of medical attention. In order to automatically identify and diagnose the fungal species of infected patients, we propose a fluorescent identification method of skin fungi with a compact model and high identification accuracy. The method uses fluorescence staining to label fungi, and data enhancement processing is applied to the collected sample images to compensate for the shortcomings of less clinical data of some fungal samples. Convolutional Tokenizer and Positional Embedding are applied to provide sample image labeling in the recognition process, and Transformer is used to solve the problem of too many layers of traditional Convolutional Neural Networks and expand the image perceptual field; In the Sequence Pooling module, the attention mechanism is used to pool all the tokens. Finally, it is transformed into the final category output through a Fully Connected layer. The experimental results show that the method takes less training time, has a compact model and a smaller number of parameters, while still maintaining a high recognition accuracy, which is of great practical value in clinical diagnosis.

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  1. A Transformer-based Method for Skin Fungi Identification from Fluorescent Images

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    ICBIP '23: Proceedings of the 2023 8th International Conference on Biomedical Signal and Image Processing
    July 2023
    140 pages
    ISBN:9798400707698
    DOI:10.1145/3613307
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 28 September 2023

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    Author Tags

    1. convolutional tokenizer
    2. fluorescent image
    3. image preprocessing
    4. transformer encoder

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