Detection of Cancer and Comparison of Survival Rate of Patients from CT Images on Long Nodules Data using Novel Support Vector Machine and 3D Convolutional Neural Network Classification

the study aimed to improve the survival rate for lung cancer classification by comparing the Novel SVM (SVM) algorithm with the 3D Convolution neural network (3D CNN). Materials and Methods: Proposed work involves two groups for lung cancer detection. Group 1 was the Novel SVM algorithm and Group 2 was the 3D CNN algorithm. The sample size was calculated. It was identified that 20 samples/group and 40 samples were taken totally. The improved SVM algorithm was compared with the 3D CNN algorithm for improving the survival rate. Results and Discussion: Data collection was carried out the analysis can be done in the compiler for the execution of results and survival rate of a particular algorithm. Here in this proposed work, the improved Novel SVM algorithm was compared with the 3D CNN algorithm. The survival rate for SVM got significantly more accuracy (92.02%) when compared with 3D CNN which had an accuracy (67.85%) and significance value was insignificant (p=0.2). Conclusion: The data was collected from various lung cancer prediction system resources. The improved SVM algorithm was used for the whole lung cancer prediction process for a higher survival rate than the 3D CNN.