Qize Lv, Ke Liang, ChongXuan Tian, YanHai Zhang, YunZe Li, JinLin Deng, WeiMing Yue, Wei Li
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引用次数: 0
Abstract
Thymoma, a rare tumor from thymic epithelial cells, presents diagnostic challenges because of the subjective nature of traditional methods, leading to high false-negative rates and long diagnosis times. This study introduces a thymoma classification technique that integrates hyperspectral imaging with deep learning. We initially capture pathological slice images of thymoma using a hyperspectral camera and delineate regions of interest to extract spectral data. This data undergoes reflectance calibration and noise reduction. Subsequently, we transform the spectral data into two-dimensional images via the Gramian Angular Field (GAF) method. A variant residual network is then utilized to extract features and classify these images. Our results demonstrate that this model significantly enhances classification accuracy and efficiency, achieving an average accuracy of 95%. The method proves highly effective in automated thymoma diagnosis, optimizing data utilization, and feature representation learning.
期刊介绍:
The first international journal dedicated to publishing reviews and original articles from this exciting field, the Journal of Biophotonics covers the broad range of research on interactions between light and biological material. The journal offers a platform where the physicist communicates with the biologist and where the clinical practitioner learns about the latest tools for the diagnosis of diseases. As such, the journal is highly interdisciplinary, publishing cutting edge research in the fields of life sciences, medicine, physics, chemistry, and engineering. The coverage extends from fundamental research to specific developments, while also including the latest applications.