A novel myocarditis detection combining deep reinforcement learning and an improved differential evolution algorithm  

作　　者：Jing Yang Touseef Sadiq Jiale Xiong Muhammad Awais Uzair Aslam Bhatti Roohallah Alizadehsani Juan Manuel Gorriz 

机构地区：[1]Department of Computer System and Technology,Faculty of Computer Science and Information Technology,Universiti Malaya,Kuala Lumpur,Malaysia [2]Centre for Artificial Intelligence Research(CAIR),Department of Information and Communication Technology,University of Agder,Grimstad,Norway [3]Department of Creative Technologies,Air University,Islamabad,Pakistan [4]School of Information and Communication Engineering,Hainan University,Haikou,Hainan,China [5]Institute for Intelligent Systems Research and Innovation(IISRI),Deakin University,Waurn Ponds,Victoria,Australia [6]Data Science and Computational Intelligence Institute,University of Granada,Granada,Spain

出　　处：《CAAI Transactions on Intelligence Technology》2024年第6期1347-1360,共14页智能技术学报(英文)

摘　　要：Myocarditis is a serious cardiovascular ailment that can lead to severe consequences if not promptly treated.It is triggered by viral infections and presents symptoms such as chest pain and heart dysfunction.Early detection is crucial for successful treatment,and cardiac magnetic resonance imaging(CMR)is a valuable tool for identifying this condition.However,the detection of myocarditis using CMR images can be challenging due to low contrast,variable noise,and the presence of multiple high CMR slices per patient.To overcome these challenges,the approach proposed incorporates advanced techniques such as convolutional neural networks(CNNs),an improved differential evolution(DE)algorithm for pre-training,and a reinforcement learning(RL)-based model for training.Developing this method presented a significant challenge due to the imbalanced classification of the Z-Alizadeh Sani myocarditis dataset from Omid Hospital in Tehran.To address this,the training process is framed as a sequential decision-making process,where the agent receives higher rewards/penalties for correctly/incorrectly classifying the minority/majority class.Additionally,the authors suggest an enhanced DE algorithm to initiate the backpropagation(BP)process,overcoming the initialisation sensitivity issue of gradient-based methods like back-propagation during the training phase.The effectiveness of the proposed model in diagnosing myocarditis is demonstrated through experimental results based on standard performance metrics.Overall,this method shows promise in expediting the triage of CMR images for automatic screening,facilitating early detection and successful treatment of myocarditis.

关 键 词：CLASSIFICATION differential evolution MYOCARDITIS reinforcement learning 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]