## 5 Conclusions
(2019) [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] A complex study of the peculiarities of blood serum absorption of rats with experimental liver cancer. _Opt. Spectrosc._**2019**, _126_, 721-729. [CrossRef]
* [PERSON] (2022) [PERSON]; [PERSON] Machine Learning and Biomedical Sub-Terahertz/Terahertz Technology. In _Sub-Terahertz Sensing Technology for Biomedical Applications. Biological and Medical Physics, Biomedical Engineering_; Springer: Singapore, 2022. * [PERSON] and [PERSON] (2021) [PERSON]; [PERSON] Machine Learning Techniques for THz Imaging and Time-Domain Spectroscopy. _Sensors_**2021**, _21_, 1186. [CrossRef] [PubMed]
* [PERSON] et al. (2022) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Machine Learning and Application in Terahertz Technology: A Review on Achievements and Future Challenges. _IEEE Access_**2022**, _10_, 53761-53776. [CrossRef]
* [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; et al. Terahertz spectroscopic diagnosis of early blast-induced traumatic brain injury in rats. _Biomed. Opt. Express_**2020**, _11_, 4085-4098. [CrossRef]
* [PERSON] et al. (2011) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON]; et al. The human serum metabolome. _PLoS ONE_**2011**, \(6\), e16957. [CrossRef] [PubMed]
* [PERSON] et al. (2016) [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] Brain tumor imaging of rat fresh tissue using terahertz spectroscopy. _Sci. Rep._**2016**, \(6\), 30124. [CrossRef] [PubMed]
* [PERSON] et al. (2014) [PERSON], [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Study of freshly excised brain tissues using terahertz imaging. _Biomed. Opt. Express_**2014**, \(5\), 2837-2842. [CrossRef]
* [PERSON] et al. (2021) [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] Glioma Brain Tumor Grade Classification from MRI Using Convolutional Neural Networks Designed by Modified FA. In _Intelligent and Fuzzy Techniques: Smart and Innovative Solutions. INFUIS 2020. Advances in Intelligent Systems and Computing_; [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON]; Springer: Cham, Switzerland, 2021; Volume 1197. [CrossRef]
* [PERSON] et al. (2023) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Brain tumor classification using meta-heuristic optimized convolutional neural networks. _J. Pers. Med._**2023**, _13_, 181. [CrossRef]
* [PERSON] et al. (2024) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Brain tumor segmentation based on optimized convolutional neural network and improved chimp optimization algorithm. _Comput. Biol. Med._**2024**, _168_, 107723. [CrossRef]
* Available online: [[https://www.teravill.lt/datasheets/T-SPEC_20190201.pdf](https://www.teravill.lt/datasheets/T-SPEC_20190201.pdf)]([https://www.teravill.lt/datasheets/T-SPEC_20190201.pdf](https://www.teravill.lt/datasheets/T-SPEC_20190201.pdf)) (accessed on 1 February 2024). * [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] Diagnosis of oral lichen plants from analysis of saliva samples using terahertz time-domain spectroscopy and chemometrics. _J. Biomed. Opt._**2018**, _23_, 045001. [CrossRef]  
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