Pulmonary Nodules Classification In Computed Tomography Images Using Faster R-CNN

Journal of Pharmaceutical Negative Results ¦ Volume 13 ¦ Special Issue 10 ¦ 2022 1514
Pulmonary Nodules Classification In Computed
Tomography Images Using Faster R-CNN
Anitha. S1, Hemavathi. P.V2, Sethu. S3, Subbulakshmi. M4, Packialatha .A5, Vijitha. S6
1sanitha.se@velsuniv.ac.in
CSE ,Department Vels Institute of Science, Technology& Advanced Studies
2pvhemavathi.se@velsuniv.ac.in
CSE ,Department ,Vels Institute of Science, Technology& Advanced Studies
3sethu.se@velsuniv.ac.in
CSE ,Department ,Vels Institute of Science, Technology& Advanced Studies
4subbulakshmi.se@velsuniv.ac.in
CSE ,Department, Vels Institute of Science, Technology& Advanced Studies
5packialatha.se@velsuniv.ac.in
CSE ,Department, Vels Institute of Science, Technology& Advanced Studies
6vijitha.se@velsuniv.ac.in
CSE ,Department, Vels Institute of Science, Technology& Advanced Studies
DOI: 10.47750/pnr.2022.13.S10.177
Pulmonary nodules are the primary symptom of lung cancer, which has a high mortality rate anywhere in the globe. Radiologists
are able to spend less time on false positives and missed diagnoses because to automatic pulmonary nodule identification. We
suggest using a more efficient variant of the Faster R-CNN algorithm to identify these pulmonary nodules in early stages. A more
efficient Faster R-CNN algorithm is able to identify pulmonary nodules, and this is shown by using the training set. Theoretically,
modification of the parameters involved in a network may lead to both structural and detection improvements. This research
proposes an enhanced and optimized approach for identifying pulmonary nodules, which, on average, increases detection accuracy
by over 20% compared to previous, more conventional techniques. The Faster R-CNN-based technique of pulmonary nodule
detection demonstrated high accuracy in our experiments, suggesting it may have practical use in the diagnosis of pulmonary
illness. This technique may be of additional use to radiologists and academics working on improving the pulmonary nodule’s
detection lesion.