Deploying Deep Learning in Real-Time for Lung Cancer Diagnosis via Medical Imaging
subject: Automated; Deep learning; Diagnosis; Lung cancer; Medical imaging
description: In this research, deep learning models were used to diagnose lung cancer automatically using hospital image data. A dataset with 3,400 lung cancer images from online repositories and hospital archives was used for model training and evaluation. After preprocessing and feature extraction, various deep learning architectures such as VGG-16, CNN, ResNet and RNN were adopted in this study. The VGG-16 model had the highest accuracy rate of 96.86%, showing strong performance. This rate of accuracy is actually higher than their accuracy of 91%. These results serve to highlight the impressive accuracy achieved by our study relative to prior research. By accurately and effectively altering lung cancer diagnosis into a process entirely reliant on algorithms, deep learning models show promise for their potential. Diagnostic tools should be able to catch cancer early and accurately, identify the present type and classification for tumors. For all its promise, limitations such as dataset size and generalizability mean that clinical trials will be needed for further validation. Focus should turn toward this as the direction of future research in order to enhance model robustness and applicability against challenges. This research allows us to better the well-being of patients and reduce the burden of lung cancer through timely intervention and personalized treatment strategies by making use of advanced techniques in medical diagnostics. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
creator: George, Jossy P.; Upreti, Kamal; Poonia, Ramesh Chandra; Alapatt, Bosco Paul
source: Lecture Notes in Networks and Systems;Volume;1144;pp.401-412
publisher: Springer Science and Business Media Deutschland GmbH
date: 01-01-2025
rights: Restricted Access; Hardcopy may be available in the library
relation: ISSN: 23673370; ISBN: 978-981977838-6;
language: English
type: Conference paper
coverage: George J.P., Department of Computer Science, CHRIST (Deemed to Be University), Delhi NCR, Uttar Pradesh, Ghaziabad, India; Upreti K., Department of Computer Science, CHRIST (Deemed to Be University), Delhi NCR, Uttar Pradesh, Ghaziabad, India; Poonia R.C., Department of Computer Science, CHRIST (Deemed to Be University), Delhi NCR, Uttar Pradesh, Ghaziabad, India; Alapatt B.P., Department of Computer Science, CHRIST (Deemed to Be University), Delhi NCR, Uttar Pradesh, Ghaziabad, India