Enhanced detection of ovarian cancer using AI-optimized 3D CNNs for PET/CT scan analysis.
Authors
Affiliations (6)
Affiliations (6)
- Nuclear Engineering Department, School of Mechanical Engineering, Shiraz University, Shiraz, Iran.
- Nuclear Engineering Department, School of Mechanical Engineering, Shiraz University, Shiraz, Iran. [email protected].
- Radiation Research Center, School of Mechanical Engineering, Shiraz University, Shiraz, Iran. [email protected].
- Radiation Research Center, School of Mechanical Engineering, Shiraz University, Shiraz, Iran.
- Ionizing and Non-Ionizing Radiation Protection Research Center, School of Paramedical Sciences, Shiraz University of Medical Sciences, Shiraz, Iran.
- Nuclear Medicine and Diagnostic Imaging Section, Division of Human Health, International Atomic Energy Agency, Vienna, Austria.
Abstract
This study investigates how deep learning (DL) can enhance ovarian cancer diagnosis and staging using large imaging datasets. Specifically, we compare six conventional convolutional neural network (CNN) architectures-ResNet, DenseNet, GoogLeNet, U-Net, VGG, and AlexNet-with OCDA-Net, an enhanced model designed for [<sup>18</sup>F]FDG PET image analysis. The OCDA-Net, an advancement on the ResNet architecture, was thoroughly compared using randomly split datasets of training (80%), validation (10%), and test (10%) images. Trained over 100 epochs, OCDA-Net achieved superior diagnostic classification with an accuracy of 92%, and staging results of 94%, supported by robust precision, recall, and F-measure metrics. Grad-CAM ++ heat-maps confirmed that the network attends to hyper-metabolic lesions, supporting clinical interpretability. Our findings show that OCDA-Net outperforms existing CNN models and has strong potential to transform ovarian cancer diagnosis and staging. The study suggests that implementing these DL models in clinical practice could ultimately improve patient prognoses. Future research should expand datasets, enhance model interpretability, and validate these models in clinical settings.