Version 1
: Received: 26 January 2024 / Approved: 26 January 2024 / Online: 29 January 2024 (09:44:57 CET)
Version 2
: Received: 13 May 2024 / Approved: 13 May 2024 / Online: 14 May 2024 (08:04:26 CEST)
Version 3
: Received: 9 August 2024 / Approved: 9 August 2024 / Online: 9 August 2024 (16:00:29 CEST)
How to cite:
Lazzini, G.; D’Acunto, M. Chondrogenic Cancer Grading by combining Machine and Deep Learning with Raman Spectra of Histopathological Tissues. Preprints2024, 2024011935. https://doi.org/10.20944/preprints202401.1935.v1
Lazzini, G.; D’Acunto, M. Chondrogenic Cancer Grading by combining Machine and Deep Learning with Raman Spectra of Histopathological Tissues. Preprints 2024, 2024011935. https://doi.org/10.20944/preprints202401.1935.v1
Lazzini, G.; D’Acunto, M. Chondrogenic Cancer Grading by combining Machine and Deep Learning with Raman Spectra of Histopathological Tissues. Preprints2024, 2024011935. https://doi.org/10.20944/preprints202401.1935.v1
APA Style
Lazzini, G., & D’Acunto, M. (2024). Chondrogenic Cancer Grading by combining Machine and Deep Learning with Raman Spectra of Histopathological Tissues. Preprints. https://doi.org/10.20944/preprints202401.1935.v1
Chicago/Turabian Style
Lazzini, G. and Mario D’Acunto. 2024 "Chondrogenic Cancer Grading by combining Machine and Deep Learning with Raman Spectra of Histopathological Tissues" Preprints. https://doi.org/10.20944/preprints202401.1935.v1
Abstract
The present paper explored the possibility of discriminating between different malignant degrees of Chondrosarcoma (CS), a diffused bone tumor, and Enchondroma (EC), its benign version, through an approach based on the coupling between Confocal Raman Microscopy (CRM) and Machine Learning (ML) techniques. Recently, Raman Spectroscopy has proven to be a powerful tool for a complete grading of CS by distinguishing between grade 1, 2 and 3, and to distinguish CS from EC. In this paper, we tested some models, belonging either to ML or to the sub-type called Deep Learning (DL), showing excellent classification performances, especially the DL algorithms, with classification accuracy approaching to 100%, making the models promising for future implementations of Raman spectroscopy when applied to oncology diagnosis. In turn, we highlighted how some proper ML models result in worse classification performances but better resolution of specific chemical target compounds, possible candidate to become malignant markers, with relevant implication for a correct diagnosis.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
