Feline Wolf Net: A Hybrid Lion-Grey Wolf Optimization Deep Learning Model for Ovarian Cancer Detection

Ovarian cancer is a major cause of mortality among gynecological malignancies, emphasizing the critical role of early detection in improving patient outcomes. This paper presents an automated computer-aided design system that combines deep learning techniques with an optimization mechanism for accurate ovarian cancer detection that utilizes pelvic CT images dataset. The key contribution of this work is the development of an optimized Bi-directional Long Short-Term Memory (Bi-LSTM) model which is introduced in the layers of CNN (Convolutional Neural Network), enhancing the learning process. Additionally, a feature selection method based on Lion with Grey Wolf Optimization (LGWO) is employed to enhance classifier efficiency and accuracy. The proposed approach classifies ovarian tumors as benign or malignant using the Bi-LSTM model, evaluated on the Ovarian Cancer University of Kaggle dataset. Results showcase the effectiveness of the method, achieving remarkable performance metrics, including 98% accuracy, 99.7% recall, 93% precision, and an impressive F1 score of 98%. The proposed method's efficiency is validated through comparison with validating data, demonstrating consistent and reliable results. The study's significance lies in its potential to provide an accurate and efficient solution for early ovarian cancer detection. By leveraging deep learning and optimization, the proposed method outperforms existing approaches, highlighting the promise of advanced computational techniques in improving healthcare outcomes. The findings contribute to the field of ovarian cancer detection, emphasizing the value of integrating cutting-edge technologies for effective medical diagnosis.