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Affrose, Affrose
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Computer Science & IT Electrical & Electronics Engineering

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Journal : International Journal of Electrical and Computer Engineering

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Hybrid deep learning (ILeS-Net) for lung cancer classification in cloud-IoT healthcare systems Affrose, Affrose; Kumar, Cheruku Sandesh; Kumar, Archek Praveen
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i3.pp1588-1607

Abstract

This study presents a cloud–Internet of Things (cloud-IoT) based intelligent decision support framework for lung cancer classification and treatment recommendation, centered on a hybrid deep learning model termed ILeS-Net. Computed tomography (CT) images from a benchmark dataset are first preprocessed using Gaussian filtering to enhance image quality. Cancerous regions are identified using an Improved BIRCH (I-BIRCH) segmentation approach, followed by feature extraction using shape descriptors, color features, and Improved local Gabor XOR pattern (I-LGXP) textures. The extracted features are classified using ILeS-Net, which integrates Improved LeNet-5 and SqueezeNet architectures to achieve improved classification performance with reduced overfitting. Based on the classification results, the framework provides supportive recommendations to assist clinical decision-making. Experimental results demonstrate that the proposed ILeS-Net model achieves a maximum accuracy of 0.951, outperforming several conventional and state-of-the-art methods. The cloud–IoT integration further enables scalable, real-time, and secure data processing, highlighting the framework’s potential for practical computer-aided lung cancer diagnosis.
Co-Authors Kumar, Archek Praveen Kumar, Cheruku Sandesh