Automated prediction of endometriosis using deep learning
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⤵ 10 in-corpus citations
Abstract
Endometriosis is the anomalous progress of cells at the outer part of the uterus. Generally, this endometrial tissue stripes the uterine cavity. The existence of endometriosis is identified through procedures known as Transvaginal Ultra Sound Scan (TVUS), Magnetic Resonance Imaging (MRI), Laparoscopic procedures, and Histopathological slides. Minimal Invasive Surgery (MIS) Laparo-scopic images are recorded in a small camera. To assist the surgeon in identifying their presence of endometriosis, image quality (characteristics) was enhanced for more visual clarity. Deep learning has the ability in recognising the images for classification. The Convolutional Neural Networks (CNNs) perform classification of images on large datasets. The proposed system evaluates the performance by a novel approach that implements the transfer learning model on a well-known architecture called ResNet50. The proposed system train the model on ResNet50 architecture and yielded a training accuracy of 91%, validation accuracy of 90%, precision of 83%, and recall of 82%, which can be applied for larger datasets with better performance. The presented system yields higher Area Under Curve (AUC) of about 0.78. The proposed method yields better performance using ResNet50 compared to other transfer learning techniques.
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Cited by (10)
- Unpaired multi-modal training and single-modal testing for detecting signs of endometriosis 2025
- Deep learning based detection of endometriosis lesions in laparoscopic images with 5-fold cross-validation 2025
- Deep Learning Improves Accuracy of Laparoscopic Imaging Classification for Endometriosis Diagnosis 2024
- Revolutionizing endometriosis treatment: automated surgical operation through artificial intelligence and robotic vision 2024
- Automatic segmentation of deep endometriosis in the rectosigmoid using deep learning 2024
- Abordagem Computacional Baseada em Deep Learning para o Diagnóstico de Endometriose Profunda através de Imagens de Ressonância Magnética 2023
- On the use of Raman Blood Spectroscopy and Prediction Machines for Enhanced Care of Endometriosis Patients 2023
- Deep Learning Improves Accuracy of Laparoscopic Imaging Classification for Endometriosis Diagnosis 2023
- Automated segmentation of endometriosis using transfer learning technique 2022
- Automated segmentation of endometriosis using transfer learning technique 2022
Source provenance
- openalex
- last seen: 2026-06-04T00:00:01.174412+00:00
License: CC0
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