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Deep Learning For Enhanced Detection and Characterization Of Pulmonary Nodules

Publication Type : Conference Proceedings

Publisher : IEEE

Source : 2023 4th International Conference on Intelligent Technologies (CONIT)

Url : https://doi.org/10.1109/conit61985.2024.10626349

Campus : Nagercoil

School : School of Computing

Year : 2024

Abstract : This study aims to develop, an effective customized Convolutional Neural Network model that can accurately identify areas on CT scans that have lung nodules and those that do not. The study utilizes two datasets, the LUNA-16 dataset for training and the IQ-OTH/NCCD dataset for testing. Additionally, we employ a U-Net model for precise lung nodule segmentation. This segmentation approach aimed to achieve a comprehensive characterization of nodules based on their spatial characteristics. The testing phase involves evaluating the performance of our trained models on the IQ-OTH/NCCD dataset, particularly focusing on localizing nodules in malignant cases. Overall, the research highlights the effectiveness of deep learning in early detection and diagnosis of lung cancer. This work contributes to improving the accuracy and reliability of automated pulmonary nodule detection systems, with a focus on identifying nodules in patients with suspected malignancies.

Cite this Research Publication : G Lavanya, M Muthulakshmi, Manohar Latha, A Keerthinathan, P Vishal Krishh, Sheba Sulthana, Meka Kavya Uma Meghana, Deep Learning For Enhanced Detection and Characterization Of Pulmonary Nodules, 2023 4th International Conference on Intelligent Technologies (CONIT), IEEE, 2024, https://doi.org/10.1109/conit61985.2024.10626349

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