Abstract
Ovarian cyst identification is a critical task in gynecological diagnostics, aiming to distinguish between benign and malignant cysts for timely and appropriate treatment. Conventional models often use hand-engineered features, but this approach fails to capture the rich variability and detailed complexity found in ultrasound and MRI images. Additionally, classical approaches are prone to lower accuracy and poor generalizability across diverse patient populations and imaging conditions. Thus, this paper introduces a Quantum-inspired Levenberg–Marquardt Network (Quantum-LMNet) for ovarian cyst detection. Initially, the preprocessing is done using the Contra-Harmonic Mean Filter. The segmentation of cyst regions is carried out with AuxSegNet, employing multiple loss functions including multi-label soft margin loss, Dice loss, and focal loss. Features are subsequently derived from the Discrete Curvelet Transform, Local Quantized Patterns (LQP), and statistical metrics. Finally, ovarian cysts are identified using the proposed Quantum-LMNet, which combines the Levenberg–Marquardt Network (LMNet), with Quantum-Inspired Convolutional Neural Networks (QuCNet). The Quantum-LMNet also showed notable enhancement across several evaluation metrics, including Accuracy, True Positive Rate (TPR), True Negative Rate (TNR), False Omission Rate (FOR), Matthews Correlation Coefficient (MCC), and Cohen’s Kappa, achieving values of 96.157%, 95.720%, 97.007%, 0.099, 0.970, and 95.359%, respectively.