An Efficient Deep Learning Model for Intraoperative Tissue Classification in Gynecological Cancer

Ovarian Cancer is a formidable adversary in the realm of women's health, often eluding early detection and posing significant challenges for effective treatment. Leveraging the potential of artificial intelligence (AI) and machine learning (ML) to improve the accuracy and effectiveness of diagnosis has become imperative in the fight against this silent killer. This research presents an innovative approach known as CNNGWO, which combines the power of Convolutional Neural Networks (CNNs) with the optimization capabilities of Grey Wolf Optimization (GWO) to enhance the diagnostic accuracy of Ovarian Cancer. In this work, we explore the fusion of CNNs, a deep learning architecture renowned for its proficiency in image analysis, and GWO, a nature-inspired optimization technique inspired by the social behavior of grey wolves. The CNN component is employed for the automatic extraction of intricate patterns and features from histopathological images, providing a foundation for precise cancer detection. GWO plays a pivotal role in fine-tuning the CNN model's hyperparameters, optimizing its performance, and enhancing its predictive accuracy. Through rigorous experimentation and validation, we demonstrate the effectiveness of the CNNGWO framework in Ovarian Cancer diagnosis. The integration of these cutting-edge technologies yields remarkable results, with the model consistently outperforming traditional methods with an accuracy of 0.98. Accuracy rates, precision, recall, and F1-scores all exhibit substantial improvements, signifying the potential of CNNGWO as a valuable tool in early cancer detection.