Connected Health: IoT and AI-driven CNNs for Lung Cancer Early Detection
| International Journal of Electronics and Communication Engineering |
| © 2024 by SSRG - IJECE Journal |
| Volume 11 Issue 12 |
| Year of Publication : 2024 |
| Authors : Shajeni Justin, Tamil Selvan |
10.14445/23488549/IJECE-V11I12P125
How to Cite?
Shajeni Justin, Tamil Selvan, "Connected Health: IoT and AI-driven CNNs for Lung Cancer Early Detection," SSRG International Journal of Electronics and Communication Engineering, vol. 11, no. 12, pp. 272-282, 2024. Crossref, https://doi.org/10.14445/23488549/IJECE-V11I12P125
Abstract:
Lung cancer is gaining prominence and has remained at the top of the list of causes of mortality. The research paper focuses on early lung cancer detection and prediction using advanced AI techniques—Convolutional Neural Networks—and IoT applications. IoT devices can be installed to collect various kinds of data like real-time, kinetic, and genomic parameters. These datasets are then applied with AI to create algorithms that can interpret the datasets to achieve the detection and prediction of correct models and functions. Deep convolutional neural networks have huge potential in executing highly accurate analyses related to lung CT scans. The technical architecture and design considerations that would be important in building strong diagnostic systems will be reviewed in this paper. It also addresses current challenges, such as the need for extensive training data, validation across diverse populations, model behaviour interpretability, and integration into clinician workflows. The authors suggest that this field must be advanced through much greater clinician collaboration, synthetic data generation, privacy-preserving algorithms, predictive modelling of therapeutic responses, more streamlined regulatory approval procedures, and so on. Although this still requires more large-scale clinical trials, AI- and IoT-based early lung cancer screening techniques can improve patient outcomes while reducing healthcare costs.
Keywords:
Computer-Aided Diagnosis, Convolutional Neural Networks, Deep learning, Early diagnosis, Internet of Things, Lung cancer, Machine learning.
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