Deploying deep convolutional neural network to the battle against cancer: Towards flexible healthcare systems

Abstract

The complexity of the facilities of healthcare providers goes beyond their physical articulation, function, and organization; it also involves integrating technology and healthcare activities that continuously evolve due to medical research and technological advancements. As a result, hospitals require a flexible approach that can accommodate the changing demands of patients, medical professionals, and researchers. This flexibility is essential in ensuring that hospitals can meet the diverse needs of their users and adapt to fast-changing medical requirements. Therefore, integrating analytical capabilities of Machine Learning algorithms in healthcare services is a vital aspect of Flexible Healthcare Systems. Furthermore, it enables hospitals to efficiently organize patient data and optimize treatment plans by analyzing vast amounts of patient data. In this paper, we explored the role of Machine Learning by applying Deep Convolutional Neural Networks on three unique datasets to predict the risk of developing cancer using health informatics and to demonstrate how computer-based vision can improve cancer prognosis by analyzing medical images. Furthermore, we have employed advanced CNNs for high-accuracy cancer detection in images, using a streamlined model that combines feature-detecting convolutional layers with complexity-reducing pooling layers which ensures effective cancer identification. The implementation of these models into healthcare delivery can potentially improve patient outcomes and system-level efficiencies, but carefully considering their limitations and ethical implications are essential.