Aim: This work is a comparative study of region based novel convolutional neural networks (R-CNN) and long short term memory networks (LSTM) to improve the Cambridge face memory test accuracy. Materials and Methods: Region based novel convolutional neural network algorithm (N=20) and long short term memory network (N=20) methods are simulated by varying the NCNN parameter and long short term parameter to optimize the pH sample size is calculated using G power 80% for two groups and there are 40 sample used in this work. Result: Based on obtained results NCNN has significantly better accuracy (91.62%) compared to long short term accuracy (89.19%). There exists statistical insignificance between region based novel convolutional neural network and long short term memory network based on independent sample T-test with the value of p=0.453 (p>0.05). Conclusion: Region based novel convolutional neural network algorithm produces better results in detecting face masks to improve accuracy percentage of Cambridge Face Memory Test than long short term memory algorithm.