Aim: To improve the accuracy in detecting road lanes with Receiver Operating Characteristic using Noval Scale-Invariant Feature Transform over Convolutional Neural Network. Materials and Methods: This study contains 2 groups i.e Scale-Invariant Feature Transform (SIFT) and Convolutional Neural Network (CNN) Each group consists of a sample size of 5301 and the study parameters include alpha value 0.05, beta value 0.2, and the power value 0.8. Their accuracies are also compared with each other using different sample sizes. Results: The Noval Scale-Invariant Feature Transform has an accuracy of 92.38% and the Convolutional Neural Network of 84.2% in Road Lane Detection. The significance value for performance and loss is 0.965 (p>0.05) Conclusion: The SIFT model is significantly better than the CNN in identifying Road Lane Detection. It can be also considered as a better option for the Lane Detection in General.