Deep Neural Transfer Network Technique for Lung Cancer Detection

Reddy, B. Dinesh; Rao, N. Thirupathi; Bhattacharyya, Debnath

doi:10.1007/978-981-99-0085-5_20

Cited by 1 publication

(1 citation statement)

References 18 publications

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“…The proposed method achieves the lowest loss value, indicating superior predictive accuracy. Conversely, models such as Xception and Inception V3 exhibit significantly higher loss values [40,41]. This evaluation demonstrates that our approach outperforms other deep learning models in diagnosing various types of lung cancer using the datasets and collected data.…”

Section: B Obtained Resultsmentioning

confidence: 70%

Deep Learning-Powered Lung Cancer Diagnosis: Harnessing IoT Medical Data and CT Images

Zhang,

Wang,

Huang

et al. 2024

IJACSA

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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.

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Section: B Obtained Resultsmentioning

confidence: 70%