Predictive Modeling of Eye Cancer Using Deep Learning Techniques: An Analysis

one such application is the Detection and Diagnosis of Eye cancer disease with the help of Deep learning techniques in Medical Imaging. Overview: Eye cancer ocular melanoma is a rare cancer that grows within the cells inside your eye. Detecting and diagnosing AD early on is key for successful treatment and patient outcomes. Consequently, there is a rise in the interest of utilizing deep learning methods to develop predictive models for early eye cancer detection and diagnosis. In this paper, we have conducted a comprehensive survey on eye cancer prediction using deep learning tools to understand the current state-of-the-art predictive modeling. We start by studying the related works on applying deep learning in medical imaging, specifically for eye cancer detection. We then delve into the varying deep learning architectures and methodologies incredibly successful in this quest, including convolutional neural networks CNNs or recurrent neural networks RNNs. We also discuss these models' performant data sources and preprocessing methods. We then evaluate the models based on accuracy, sensitivity, specificity, and other measures. We present here the strengths and weaknesses of each approach and recommendations for future work in this area.