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Cloud-Based Lung Tumor Detection and Stage Classification Using Deep Learning Techniques by Gopi Kasinathan; Selvakumar Jayakumar is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

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Artificial intelligence (AI), Internet of Things (IoT), and the cloud computing have recently become widely used in the healthcare sector, which aid in better decision-making for a radiologist. PET imaging or positron emission tomography is one of the most reliable approaches for a radiologist to diagnosing many cancers, including lung tumor. In this work, we proposed stage classification of lung tumor which is a more challenging task in computer-aided diagnosis. As a result, a modified computer-aided diagnosis is being considered as a way to reduce the heavy workloads and second opinion to radiologists. In this paper, we present a strategy for classifying and validating different stages of lung tumor progression, as well as a deep neural model and data collection using cloud system for categorizing phases of pulmonary illness. The proposed system presents a Cloud-based Lung Tumor Detector and Stage Classifier (Cloud-LTDSC) as a hybrid technique for PET/CT images. The proposed Cloud-LTDSC initially developed the active contour model as lung tumor segmentation, and multilayer convolutional neural network (M-CNN) for classifying different stages of lung cancer has been modelled and v

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Author

Gopi Kasinathan; Selvakumar Jayakumar

Publisher

Hindawi Limited

Published

2022

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)