article · Nature Journal of Emerging Sciences Technologies and Innovations
Endometriosis diagnosis often relies on laparoscopy, an invasive procedure where varied lesion appearances can lead to visual misidentification or missed diagnoses. To improve prediction accuracy, this study evaluates deep learning and transfer learning models, specifically a standard convolutional neural network alongside ResNet101V2, MobileNet, and VGG16. The architectures integrate the Pelican Optimisation Algorithm to select predominant visual features from a dataset of 25,683 laparoscopic images sourced from the Gynecologic Laparoscopy Endometriosis repository. Across comparative experiments classifying pathological versus non-pathological tissue, the transfer learning models combined with the optimisation algorithm consistently outperformed the standard network. Among these, the MobileNet variant demonstrated the highest diagnostic performance, attaining 100 percent general accuracy, 99.5 percent precision, 99.5 percent recall, and an F1-score of 100 percent.
Visual diagnosis of endometriosis during surgery is challenging because lesions frequently vary in appearance or can be missed entirely. Developing highly accurate automated image classification models can assist medical professionals in identifying disease earlier and more reliably, potentially reducing complications such as infertility, depression, and risks associated with advanced disease stages.
The model could support medical software developers creating diagnostic assistance tools for gynaecologists and surgeons interpreting laparoscopic imaging. The research appears to be applied and tested on an existing dataset of laparoscopic images, but the abstract does not indicate that clinical validation or software integration into hospital environments has yet taken place.
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The prevalence of endometriosis is underestimated because of the need for laparoscopy an invasive diagnostic method, which is considered the gold standard. Advanced stages of endometriosis may lead to endometrial cancer, infertility, psychological depression, leading to further complications. Endometriosis has multiple appearances; the lesions may be confused with other non-endometriotic lesions or endometriotic lesions that are non-endometriotic by appearance, or deep infiltrating ones may be missed on visual diagnosis. Therefore, this research aims to develop an endometriosis prediction by utilizing four different deep and transfer learning architecture including CNN, RestNet101V2, MobileNet, and VGG16 The proposed model employs Pelican Optimization Algorithm (POA) to extract predominant features for CNN, ResNet101V2, MobileNet, and VGG16 endometriosis classification. Image Dataset was obtained from Gynecologic Laparoscopy Endometriosis (GLENDA) repository containing 25,683 sample laparoscopic images of both pathological and non-pathological identified endometriosis regions. The experimental analysis revealed that POA_ResNet101V2, POA_MobileNet, and POA_VGG16 perform significantly better than CNN during the classification of endometriosis (pathology and non-pathology). Betterstill, MobileNet achieved a general accuracy of 100%, precision 99.5%, Recall 99.5%, and F1-score of 100%. The model demonstrates the effectiveness of transfer learning, MobileNet better than other transfer Learning in the existing studies. To address the diagnostic challenges of endometriosis, this study developed an optimized endometriosis prediction model with deep and transfer learning techniques, perform comparative analysis on the developed model and benchmark the results with existing ones. This model will assist health practitioners to early detect endometriosis and proffer appropriate solutions for patients.
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DOI: 10.65752/eq1tse55
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