Performance of Data Enhancements and Training Optimization for Neural Network: A Polyp Detection Case Study

Abstract

Deep learning using neural networks is becoming more and more popular. It is frequently used in areas like video analysis, image retrieval, traffic forecast and speech recognition. In this respect, the learning and training process usually requires a lot of data. However, in many areas, data is scarce which is definitely the case in our medical application scenario, i.e., polyp detection in the gastrointestinal tract. Here, colorectal cancer is on the list of most common cancer types, and often, the cancer arises from benign, adenomatous polyps containing dysplastic cells. Detection and removal of polyps can therefore prevent the development of cancer. Due to high cost, time consumption, patient discomfort and inaccuracy of existing procedures, researchers have started to explore systems for automatic polyp detection to assist and automate current examination procedures. Following the current gained traction for neural networks, and the typical lack of medical data, we explore how data enhancements affect the training and evaluation of the networks in terms of polyp detection accuracy and particularly if it can be used to increase the detection rate. We also experiment with how various training techniques can be used to increase performance. Our experimental results show how data enhancement and training optimization can be used to increase different aspects of the performance, but we also point out mechanisms that have no and even a negative effect.

Artificial intelligence in colorectal cancer: a review

“…Endoscopists can use a real-time automatic polyp identification technology to swiftly and accurately diagnose lesions that could be adenomas [16]. Small-scale colorectal polyps can be resected and discarded thanks to the precision of endoscopic differential diagnosis [74]. Lund Henriksen et al [74] investigated a system for automatic polyp detection to aid and automate the inspection procedures in order to alleviate the high cost, long time consuming, and patients' pain.…”

“…Small-scale colorectal polyps can be resected and discarded thanks to the precision of endoscopic differential diagnosis [74]. Lund Henriksen et al [74] investigated a system for automatic polyp detection to aid and automate the inspection procedures in order to alleviate the high cost, long time consuming, and patients' pain. When stochastic gradient descent was utilized as the training optimizer, the detection rate increased but the number of FP remained relatively consistent [74], as compared to root mean square propagation, stochastic gradient descent, and adaptive moment estimation.…”