A Breakthrough Approach for Prostate Cancer Identification Utilizing VGG-16 CNN Model with Migration Learning

In the realm of medical visual scrutiny, the accurate identification of prostate cancer holds paramount significance for early diagnosis and effective treatment. This work presents a pioneering method for prostate cancer identification, harnessing the power of deep learning and Migration Learning strategies. Leveraging the VGG-16 Convolutional Neural Network (CNN) framework as the cornerstone, the proposed approach capitalizes on its ability to extract intricate features from medical images. By incorporating Migration Learning, the model is enriched with knowledge gleaned from diverse datasets, enabling it to achieve exceptional performance even with limited medical image data. The methodology entails meticulous dataset curation and preprocessing, ensuring the quality and representativeness of the images. The VGG-16 model undergoes a meticulous finetuning process, accommodating the unique characteristics of prostate cancer images. Performance evaluation is conducted rigorously, utilizing established metrics to gauge the approach's effectiveness. Comparative analysis with contemporary methods showcases the breakthrough potential of the proposed approach. The model gave 93.97% testing accuracy.