Abstract: Currently, lung cancer poses a significant global threat, ranking among the most perilous and lethal ailments. Accurate early detection and effective treatments play pivotal roles in mitigating its mortality rates. Utilizing deep learning techniques, CT scans offer a highly advantageous imaging modality for diagnosing lung cancer. In this study, we introduce an innovative approach employing a hybrid Deep Convolutional Neural Network (DCNN), trained on both CT scan images and medical data retrieved from IoT wearable sensors. Our method encompasses a CNN comprising 22 layers, amalgamating latent features extracted from CT scan images and IoT sensor data to enhance the detection accuracy of our model. Training our model on a balanced dataset, we evaluate its performance based on metrics including accuracy, Area under the Curve (AUC) score, loss, and recall. Upon assessment, our method surpasses comparable approaches, exhibiting promising prospects for lung cancer diagnosis compared to alternative models.

Xiao Zhang, Xiaobo Wang, Tao Huang and Jinping Sheng, “Deep Learning-Powered Lung Cancer Diagnosis: Harnessing IoT Medical Data and CT Images” International Journal of Advanced Computer Science and Applications(IJACSA), 15(4), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0150418

@article{Zhang2024,
title = {Deep Learning-Powered Lung Cancer Diagnosis: Harnessing IoT Medical Data and CT Images},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150418},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150418},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {4},
author = {Xiao Zhang and Xiaobo Wang and Tao Huang and Jinping Sheng}
}