Deep Learning Improves Accuracy of Laparoscopic Imaging Classification for Endometriosis Diagnosis

Chrysa Nifora, Nifora C, Chasapi L, Lamprini Chasapi, Chasapi M, Maria-Konstantina Chasapi, Koutsojannis C, Constantinos Koutsojannis
2023 · doi:10.20944/preprints202312.0475.v1 · W4389485528
preprint OA: green CC0 ⤵ 1 in-corpus citation
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This study developed a deep learning system using ResNet50 on 4448 laparoscopic images, achieving over 95% accuracy in classifying endometriosis and outperforming traditional imaging criteria.

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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.

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Condition tags

endometriosis

Citation neighborhood

Papers in the corpus that this work cites (lower rings, blue) and that cite this one (upper rings, green). Dot size scales with the paper's in-corpus citation count — bigger dot = more influential within the endo/adeno field. Click a dot to open that paper. [ expand to 2 hops ] — adds papers reached through this work's immediate citers/citees. Heavier; up to 60 extra dots.

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europepmc
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