2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 13 , Issue 2 , PP: 166-177, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
A. Gopinath 1 * , P.Gowthaman 2
Doi: https://doi.org/10.54216/JISIoT.130213
Today's clinical analysis and precise illness detection are mandated requirements for the development of intelligent expert systems. Since lung cancer affects both men and women equally and has a greater mortality rate than other illnesses, a more complete examination is needed to diagnose lung cancer. More helpful information regarding a lung cancer diagnosis may be provided by images from a computer tomography (CT) scan. Various machine learning and deep learning algorithms are created to enhance the medical treatment process using CT scan input pictures. But research still has a bad side when it comes to creating a precise and intelligent system. In order to improve the detection of lung tumors from the CT input images, this paper presented Firefly optimized pre trained transfer learning. The previously trained model VGG-16 is used in this paper to extract features more effectively, using the features chosen via the firefly optimization approach to increase classification accuracy while reducing complexity. The thorough testing done with the “LUNA-16 & LIDC Lung image” datasets is assessed & studied along with other performance measures like "accuracy, precision, recall, specificity, and F1-score". Investigation results show that the suggested design outperformed the “DenseNet, AlexNet, Resnets-50, Resnets-100, VGG-16 & Inception models” and reached the top results with "98.5% accuracy, 99.0% precision, 98.8% recall, with 99.1% F1-score.
Computer Tomography Images , Firefly Optimization , Vgg16 , Classification , Accuracy , Lung Tumor
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