Precision in Prostate Cancer Diagnosis: A Comprehensive Study on Neural Networks
Prostate cancer is a type of cancer that begins in the cells of the prostate in the male reproductive system. The diagnosis of prostate cancer is a crucial aspect of healthcare where precision and efficiency play a pivotal role. This research paper delves into the analysis of deep learning techniques to enhance the accuracy and effectiveness of prostate cancer diagnosis. Advanced convolutional neural network architectures like DenseNet121, MobileNetV2, and EfficientNetB0 are used operationally within this research. It was conducted on Prostate Cancer Grade Assessment (PANDA) dataset Training models individually with the dataset and getting their performances via training plots shows that DenseNet121 overtakes the other two models at an amazing 85.98% accuracy. This result demonstrates the great potential of deep learning for the improvement of diagnostic accuracy, in particular within the scenario of prostate cancer. The research offers a large amount of insight into this implementation of state-of-the-art neural network architectures on medical image classification that helps to improvements in diagnostic accuracy and treatment for prostate cancer patients.