Lung Cancer Detection using Segmented 3D Tensors and Support Vector Machines

Tumor is currently the second most prevalent cause of mortality, and its prevalence is expanding rapidly. The development of pulmonary nodules inside the lungs is suggestive of the existence of lung cancer. The detection of cancer is achieved using nodules detected in computer tomography (CT) images obtained from the LUNA 16 dataset. This study uses the Python library "PyTorch" for this purpose. A three-dimensional model has been used to train and extract the nodular segments from CT-Scan images, referred to as CT-scan chunks. It is done due to the impracticality of handling the whole CT scan image due to its vast size. The previously mentioned chunks are then transformed into PyTorch tensors. The tensors are subsequently input into a deep learning model to extract features, which are then passed through a sequence of machine learning classifiers for the purpose of classification. These classifiers include Support Vector Machines, Multi-layer Perceptron, Random Forest Classifier, Logistic Regression, K Nearest Neighbor, and Linear Discriminant Analysis. Our research has shown that the use of chunk extraction from CT-Scan images, coupled with the creation of tensors using segmented CT scans, has significantly enhanced the precision of various machine learning algorithms. Additionally, this approach has the advantage of reducing the computational time during runtime. In our study, the use of Support Vector Machines yielded the best degree of accuracy, reaching 99.68%. The findings of this study have the potential to be valuable in the practical implementation of real-time lung nodule identification applications.