Leveraging Cloud Technology for Convolutional Neural Networks in Cancer Detection and Diagnosis

Abstract

Lung cancer is one of the areas in which detection still poses some challenges for the healthcare arena. The
classical way is to analyse histopathological images by human intervention; this is a time-consuming process that
is somewhat on the error-prone side. A cloud-based convolutional neural network model has been proposed for
the diagnosis of lung cancer, specifically targeting lung adenocarcinoma via histopathological images from
TCGA_LUAD datasets. For cleaner input data, sophisticated preprocessing methods are applied like denoising
and augmentation. The Firefly Optimization Algorithm has been implemented for feature selection that consumes
the least computation. Cloud computing helps with scaling, real-time predictions, and access for healthcare
professionals, thus eliminating some constraints on local hardware resources. Notably, cancer detection with the
proposed CNN model brings efficiency to the process, increases diagnostic accuracies, and reduces the workload
of pathologists for treatment decisions. The system is designed to scale and grow with a security infrastructure,
so it will adapt and incorporate future data and advances in cancer research to obtain more timely and accurate
clinical diagnostics.