Aim: The research focuses on detecting cutaneous nevi using an innovative convolutional neural network and compare its accuracy with Techniques for Recurrent Neural Networks. Methods and Resources: We employ novel techniques involving Convolutional and Recurrent Neural Networks to predict the performance of skin nevus detectors with a total iteration value of N = 10 for skin nevus detection and its types like malignant, benign. The iteration value was obtained with G-power values of 0.8 with 95% confidence interval. The train dataset consists of a total of 1438 of benign and 1199 of malignant samples. The test dataset consists of 2 directories of 320 images in malignant, 280 images in benign. The iteration was measured as 20 per group. Result: Here, the total iteration of the algorithms is 20 and discrepancy of 0.023 with a p-value lower than 0.05 is considered statistically significant was seen between the two algorithms in terms of accuracy and loss, According to the results of a T-Test conducted on a separate group of employees. Thus, the real-time skin nevus detection has been implemented by using the Novel While Recurrent Neural Networks attained an accuracy of 73.22%, whereas Convolutional Neural Networks reached 85.80%. In summary, the research shows that when it comes to predicting skin nevus detection, the Novel Recurrent Neural Networks aren’t as effective as Convolutional Neural Networks.