Effective Classification of Colon Cancer using Resnet-50 in Comparison with Squeezenet

Abstract

Aim: The work aims to find out a better way to perform colon cancer identification with the help of Resnet-50 algorithm in comparison with a Squeezenet algorithm to maximize the accuracy in finding rather than the existing method of prediction.

Materials and Methods: A collection of 30 samples were picked for consideration; group 1 visualizing Resnet-50 classifier algorithm and finally group 2 is responsible for the Squeezenet algorithm. Thus G power calculation is done with a power of 80.5% and the alpha value is equivalent to 0.061.

Results: When compared to the Squeezenet algorithm, the Resnet-50 classifier algorithm has attained an accuracy of 87.5060 % which is somewhat greater than the existing method. (i.e. Squeezenet algorithm obtained an accuracy of 85.5433 %).

Conclusion: From the above statement it is evident that the Resnet-50 algorithm would be a better choice for predicting colon
cancer and it is more effective.