Title: Pulmonary carcinoma cells classification using a novel DCNN model integrated with cloud computing environment

Authors: R. Sudha; K.M. Uma Maheswari

Addresses: Department of Computer Science and Engineering, SRM Institute of Science and Technology, Chengalpattu, 603203, India ' Faculty of Engineering and Technology, Department of Computing Technologies, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, India

Abstract: In this work, we presented a carcinoma cells classification of Non-Small Cell Lung Cancer (NSCLC) which is a more difficult challenge in CAD detection. So, a modified CADx is being investigated to alleviate radiologists' excessive work and the need for the following interpretations. We describe an approach for identifying and verifying different types of pulmonary carcinoma. In addition, a novel deep convolutional neural network (DCNN) and data were obtained via a cloud system for classifying lung nodule cell types in this study. As an integrated approach for CT images, the presented system includes a Cloud-based Lung Carcinoma cell Classifier. The suggested Cloud Based-on LCC first applied active snake model-based segmentation. A Novel DCNN for identifying distinct malignant cells of lung nodules is designed and verified using open sources Lung images. When compared to current strategies, our suggested technique reaches an accuracy of 96%, which is higher than other models.

Keywords: artificial intelligence; cloud computing; CT scans; deep neural networks; pulmonary carcinoma.

DOI: 10.1504/IJSSE.2025.151322

International Journal of System of Systems Engineering, 2025 Vol.15 No.6, pp.597 - 613

Received: 13 May 2023
Accepted: 03 Aug 2023
Published online: 23 Jan 2026 *

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