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Title: Evaluation of Preprocessing Methods on Independent Medical Hyperspectral Databases to Improve Analysis
Authors: Martínez Vega, Beatriz 
Tkachenko, Mariia
Matkabi, Marianne
Ortega, Samuel 
Fabelo Gómez, Himar Antonio 
Balea-Fernández, Francisco Javier 
La Salvia, Marco
Torti, Emanuele
Leporati, Francesco
Marrero Callicó, Gustavo 
Chalopin, Claire
UNESCO Clasification: 330790 Microelectrónica
3302 Tecnología bioquímica
Keywords: Min-max scaling
Standard normal variate normalization
Median filter
Hyperspectral imaging
Machine learning, et al
Issue Date: 2022
Project: Talent Imágenes Hiperespectrales Para Aplicaciones de Inteligencia Artificial 
Journal: Sensors (Switzerland) 
Abstract: Currently, one of the most common causes of death worldwide is cancer. The development of innovative methods to support the early and accurate detection of cancers is required to increase the recovery rate of patients. Several studies have shown that medical Hyperspectral Imaging (HSI) combined with artificial intelligence algorithms is a powerful tool for cancer detection. Various preprocessing methods are commonly applied to hyperspectral data to improve the performance of the algorithms. However, there is currently no standard for these methods, and no studies have compared them so far in the medical field. In this work, we evaluated different combinations of preprocessing steps, including spatial and spectral smoothing, Min-Max scaling, Standard Normal Variate normalization, and a median spatial smoothing technique, with the goal of improving tumor detection in three different HSI databases concerning colorectal, esophagogastric, and brain cancers. Two machine learning and deep learning models were used to perform the pixel-wise classification. The results showed that the choice of preprocessing method affects the performance of tumor identification. The method that showed slightly better results with respect to identifing colorectal tumors was Median Filter preprocessing (0.94 of area under the curve). On the other hand, esophagogastric and brain tumors were more accurately identified using Min-Max scaling preprocessing (0.93 and 0.92 of area under the curve, respectively). However, it is observed that the Median Filter method smooths sharp spectral features, resulting in high variability in the classification performance. Therefore, based on these results, obtained with different databases acquired by different HSI instrumentation, the most relevant preprocessing technique identified in this work is Min-Max scaling.
ISSN: 1424-8220
DOI: 10.3390/s22228917
Source: Sensors (Switzerland) [ISSN 1424-8220], v. 22 (22), 8917, (2022)
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