Enhancing Prostate Cancer Prediction: A Fusion of HAAR, LBP, and SIFT Features with CNN

The suggested deep learning model is specifically developed to analyze medical images, including Magnetic Resonance Imaging (MRI) scans and histopathology slides, to detect patterns and characteristics that suggest the presence of prostate cancer. The dataset utilized for training and evaluation comprises a heterogeneous collection of prostate pictures, encompassing both malignant and benign instances. The study incorporates pre-processing techniques to improve the quality of the deep learning model. Transfer learning methods can utilize pre-trained convolutional neural network (CNN) models on extensive datasets, which helps enhance the ability to predict prostate cancer with better accuracy. The model's effectiveness is assessed using performance assessment criteria such as sensitivity, specificity, and accuracy, which are measured. The proposed technique is validated by comparing the results with existing methodologies and benchmarks in the field. The research seeks to augment the existing corpus of literature on employing deep learning methodologies for cancer prognosis and aspires to furnish a dependable instrument for timely detection of prostate cancer. The suggested CNN model has the potential to be incorporated into clinical workflows, aiding healthcare workers in making well-informed decisions.