Detection of Lung Cancer Using Watershed Algorithm and CNN

Lung cancer is a serious public health issue that requires better early detection. To establish a novel framework for lung cancer detection, we combine the Watershed Algorithm for feature extraction with the VGG-16 Convolutional Neural Network (CNN) architecture. This comprehensive system has the po...

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Bibliographic Details
Published in2023 Intelligent Computing and Control for Engineering and Business Systems (ICCEBS) pp. 1 - 6
Main Authors A, Vasanthi, G B, Pramith Kiran, B, Tharun
Format Conference Proceeding
LanguageEnglish
Published IEEE 14.12.2023
Subjects
Classification algorithms
Convolutional neural networks
Feature extraction
Lung
Lung cancer
Transfer learning
Watersheds
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DOI10.1109/ICCEBS58601.2023.10448847

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Summary:Lung cancer is a serious public health issue that requires better early detection. To establish a novel framework for lung cancer detection, we combine the Watershed Algorithm for feature extraction with the VGG-16 Convolutional Neural Network (CNN) architecture. This comprehensive system has the potential to improve diagnosis accuracy. An advanced feature extraction method, the Watershed Algorithm, segments lung areas while maintaining small features, reducing data loss during preprocessing. These segments are fed into the CNN VGG-16, which is well-known for recognizing complex patterns. To detect small lung anomalies, we fine-tune and train it using a huge dataset of lung CT scans. Our method, which incorporates the Watershed Algorithm and CNN VGG-16, provides a realistic way for early lung cancer diagnosis.
DOI:10.1109/ICCEBS58601.2023.10448847