Version 1
: Received: 5 December 2023 / Approved: 7 December 2023 / Online: 7 December 2023 (08:21:33 CET)
How to cite:
Nifora, C.; Chasapi, L.; Chasapi, M.; Koutsojannis, C. Deep Learning Improves Accuracy of Laparoscopic Imaging Classification for Endometriosis Diagnosis. Preprints2023, 2023120475. https://doi.org/10.20944/preprints202312.0475.v1
Nifora, C.; Chasapi, L.; Chasapi, M.; Koutsojannis, C. Deep Learning Improves Accuracy of Laparoscopic Imaging Classification for Endometriosis Diagnosis. Preprints 2023, 2023120475. https://doi.org/10.20944/preprints202312.0475.v1
Nifora, C.; Chasapi, L.; Chasapi, M.; Koutsojannis, C. Deep Learning Improves Accuracy of Laparoscopic Imaging Classification for Endometriosis Diagnosis. Preprints2023, 2023120475. https://doi.org/10.20944/preprints202312.0475.v1
APA Style
Nifora, C., Chasapi, L., Chasapi, M., & Koutsojannis, C. (2023). Deep Learning Improves Accuracy of Laparoscopic Imaging Classification for Endometriosis Diagnosis. Preprints. https://doi.org/10.20944/preprints202312.0475.v1
Chicago/Turabian Style
Nifora, C., Maria-Konstantina Chasapi and Constantinos Koutsojannis. 2023 "Deep Learning Improves Accuracy of Laparoscopic Imaging Classification for Endometriosis Diagnosis" Preprints. https://doi.org/10.20944/preprints202312.0475.v1
Abstract
PurposeThe purpose of this research was the development and evaluation of an intelligent assistive classification system for the recognition of endometriosis and the improvement of the accuracy of laparoscopic imaging in diagnosis. The research was developed with the use of Deep Learning approaches.Methods Data from 4448 laparoscopy images were used in a retrospective chart analysis. The data were divided into two folders, healthy and pathological including 2157 healthy and 2291 pathological images. Based on simple clinical and imaging information and criteria such as the diagnosis of endometriosis (included in an open-source dataset GLENDA of Kaggle repository), data mining algorithms were used to improve laparoscopic imaging accuracy.Results The final developed computer system based on the ResNet50 algorithm predicted the best outcome for all participants who had laparoscopic surgical therapy. The Keras tool was used and the generated code was implemented in Python programming language providing a mean accuracy of > 95 %. Conclusion The intelligent approach revealed better performance than the commonly used imaging criteria in predicting endometriosis improving the time and the total accuracy of diagnostic approaches.
Keywords
laparoscopic; imaging; endometriosis; Keras; python; deep learning
Subject
Public Health and Healthcare, Public Health and Health Services
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.
