EXPLAINABLE AI FOR COLON POLYPS DETECTION - Ramli, Nur Iman Athilah (Universiti Teknologi PETRONAS, 2021)
Artificial intelligence (AI) has rapidly developed over the past few decades and was considered to have a huge effect on all forms of technologies and everyday life. Due to the large volume of data, the usage of AI in healthcare system, has been increasing rapidly. Different AI techniques have been used in medical imaging, including, machine learning and the deep learning technique which is convolutional neural network (CNN) which have helped doctors identify diseases accurately and assess effective care for them. The use and processing of a large number of the digital images and a lot of medical records over a period of time has contributed to the development of big data. The interest in applying computer-aided diagnosis (CAD) and artificial intelligence (AI) to endoscopic evaluation in the gastrointestinal tract has been revived by recent developments in computing power, coupled with rapid growth in the quantity and availability of data. However, regulatory impediments that need to be implement before applying CAD in routine clinical practise because the complexity of AI driven clinical decision-making, which is generally regarded as a ' black box.' And as such, this study aims to create an explainable AI-based CAD method to help endoscopists distinguish polyps during endoscopy, which can justify their decisions using classification rules.