MRI-Enhanced Metastatic Ovarian Tumor Detection: Leveraging Enhanced 3D CNN and Data Augmentation for Exceptional Accuracy

Background/Aims: Metastatic Ovarian Tumor is a severe condition that can significantly impact the life span and quality of life of affected individuals. Common symptoms include hormonal imbalances, digestive system issues, pelvic pain, fertility problems, and depression. Accurate and early detection is essential for improving patient outcomes. This research aims to develop a more effective diagnostic tool using MRI and 3D Convolutional Neural Networks (CNN) to enhance early detection and diagnosis of metastatic ovarian tumors. Materials and Methods: This study leverages the power of 3D Convolutional Neural Networks (CNN) to analyze MRI scans for the detection of metastatic ovarian tumors. The proposed model employs a 3D CNN architecture, known for its effectiveness in image classification tasks. Existing approaches using 2D CNNs often fail to capture the spatial and temporal features of MRI scans, leading to information loss. To improve model performance, data augmentation techniques such as random cropping, resizing, and spatial deformation were integrated. The model was tested with the Ovarian Bevacizumab Response (OBR) dataset to ensure robustness against variations in tumor size, position, and orientation. Results: The proposed MRI-based model achieved an impressive accuracy of 98.76% in detecting metastatic ovarian tumors. This high level of accuracy demonstrates the model's potential as a valuable tool for early diagnosis and clinical applications. Conclusion: The investigation confirms that the proposed 3D CNN model, leveraging MRI datasets, significantly improves the detection accuracy of metastatic ovarian tumors. This model holds promise for clinical applications, enabling timely interventions and potentially improving the life span and quality of life for patients with ovarian cancer.