Prostate cancer detection in intelligent big data systems using omnipresent AI with Fox and Golden Jackal Optimizers

Prostate cancer is one of the most critical health concerns, making early detection essential for improved patient outcomes. In this context, this work used the ability of omnipresent AI and big data to provide accurate and fast prostate cancer detection. Golden Jackal Optimization (GJO) for hyperparameter optimization and the Fox optimizer for feature selection used to optimize the performance of CNN model. After training for five epochs, the model achieved an accuracy of 72%. Comparative analysis with models such as GRU, LSTM, and traditional classifiers demonstrates that the proposed method provides a robust and scalable solution for large-scale, data-driven healthcare applications. • Developed a prostate cancer detection framework utilizing a CNN-based deep learning model . • Employed Fox Optimizer for feature selection to enhance relevant data extraction from medical imaging. • Utilized Golden Jackal Optimizer for hyperparameter tuning of the CNN model, optimizing performance and efficiency. • Significantly improved classification accuracy for detecting prostate cancer.