%0 Journal Article %1 Tkachenko2023-up %A Tkachenko, Mariia %A Chalopin, Claire %A Jansen-Winkeln, Boris %A Neumuth, Thomas %A Gockel, Ines %A Maktabi, Marianne %D 2023 %J Cancers (Basel) %K topic_lifescience cancer cancer; classification; colorectal convolutional filter; hyperspectral imaging; learning; machine median networks; post-processing; pre-processing %N 7 %T Impact of pre- and post-processing steps for supervised classification of colorectal cancer in hyperspectral images %V 15 %X BACKGROUND: Recent studies have shown that hyperspectral imaging (HSI) combined with neural networks can detect colorectal cancer. Usually, different pre-processing techniques (e.g., wavelength selection and scaling, smoothing, denoising) are analyzed in detail to achieve a well-trained network. The impact of post-processing was studied less. METHODS: We tested the following methods: (1) Two pre-processing techniques (Standardization and Normalization), with (2) Two 3D-CNN models: Inception-based and RemoteSensing (RS)-based, with (3) Two post-processing algorithms based on median filter: one applies a median filter to a raw predictions map, the other applies the filter to the predictions map after adopting a discrimination threshold. These approaches were evaluated on a dataset that contains ex vivo hyperspectral (HS) colorectal cancer records of 56 patients. RESULTS: (1) Inception-based models perform better than RS-based, with the best results being 92\% sensitivity and 94\% specificity; (2) Inception-based models perform better with Normalization, RS-based with Standardization; (3) Our outcomes show that the post-processing step improves sensitivity and specificity by 6.6\% in total. It was also found that both post-processing algorithms have the same effect, and this behavior was explained. CONCLUSION: HSI combined with tissue classification algorithms is a promising diagnostic approach whose performance can be additionally improved by the application of the right combination of pre- and post-processing.

@article{Tkachenko2023-up, abstract = {BACKGROUND: Recent studies have shown that hyperspectral imaging (HSI) combined with neural networks can detect colorectal cancer. Usually, different pre-processing techniques (e.g., wavelength selection and scaling, smoothing, denoising) are analyzed in detail to achieve a well-trained network. The impact of post-processing was studied less. METHODS: We tested the following methods: (1) Two pre-processing techniques (Standardization and Normalization), with (2) Two 3D-CNN models: Inception-based and RemoteSensing (RS)-based, with (3) Two post-processing algorithms based on median filter: one applies a median filter to a raw predictions map, the other applies the filter to the predictions map after adopting a discrimination threshold. These approaches were evaluated on a dataset that contains ex vivo hyperspectral (HS) colorectal cancer records of 56 patients. RESULTS: (1) Inception-based models perform better than RS-based, with the best results being 92\% sensitivity and 94\% specificity; (2) Inception-based models perform better with Normalization, RS-based with Standardization; (3) Our outcomes show that the post-processing step improves sensitivity and specificity by 6.6\% in total. It was also found that both post-processing algorithms have the same effect, and this behavior was explained. CONCLUSION: HSI combined with tissue classification algorithms is a promising diagnostic approach whose performance can be additionally improved by the application of the right combination of pre- and post-processing.}, added-at = {2024-09-10T10:41:24.000+0200}, author = {Tkachenko, Mariia and Chalopin, Claire and Jansen-Winkeln, Boris and Neumuth, Thomas and Gockel, Ines and Maktabi, Marianne}, biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/2b36ee879f7c50300b99c509c7a5f2f22/scadsfct}, interhash = {0e5eae9983317cf80cef0b48c896b666}, intrahash = {b36ee879f7c50300b99c509c7a5f2f22}, journal = {Cancers (Basel)}, keywords = {topic_lifescience cancer cancer; classification; colorectal convolutional filter; hyperspectral imaging; learning; machine median networks; post-processing; pre-processing}, language = {en}, month = apr, number = 7, timestamp = {2024-11-28T17:41:26.000+0100}, title = {Impact of pre- and post-processing steps for supervised classification of colorectal cancer in hyperspectral images}, volume = 15, year = 2023 }