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This paper is created to explore deep learning models and algorithms thatresults in highest accuracy in detecting polyp on colonoscopy images. Previousstudies implemented deep learning using convolution neural network (CNN)algorithm in detecting polyp and non-polyp. Other studies used dropout, anddata augmentation algorithm but mostly not checking the overfitting, thus,include more than four-layer modelss. Rulei Yu et.al from the Institute ofSoftware, Chinese Academy of Sciences said that transfer learning is bettertalking about performance or improving the previous used algorithm. Mostespecially in applying the transfer learning in feature extraction. Series ofexperiments were conducted with only a minimum of 4 CNN layers applyingprevious used models and identified the model that produce the highestpercentage accuracy of 98% among the other models that apply transfer learning.Further studies could use different optimizer to a different CNN modelstoincrease accuracy.

Exploration of Various Deep Learning Models for Increased Accuracy in Automatic Polyp Detection