Myocarditis Diagnosis: A Method using Mutual Learning-Based ABC and Reinforcement Learning

• Published in: IEEE International Symposium on Computational Intelligence and Informatics pp. 000265 - 000270
• Main Authors: Danaei, Saba, Bostani, Arsam, Moravvej, Seyed Vahid, Mohammadi, Fardin, Alizadehsani, Roohallah, Shoeibi, Afshin, Alinejad-Rokny, Hamid, Nahavandi, Saeid
• Format: Conference Proceeding
• Language: English
• Published: IEEE 21.11.2022
• Subjects: ABC algorithm; Classification; Deep reinforcement learning; MRI; Myocarditis diagnosis
• ISSN: 2471-9269
• DOI: 10.1109/CINTI-MACRo57952.2022.10029403

Myocarditis occurs when the heart muscle becomes inflamed and inflammation occurs when your body's immune system responds to infections. It can be diagnosed using cardiac magnetic resonance image (MRI), a non-invasive imaging technique with the possibility of operator bias. This paper proposes a hybrid method of deep reinforcement learning-based algorithms and meta-heuristics algorithms. A mutual learning-based artificial bee colony (ML-ABC) is employed for initial weight, which adjusts the candidate food source generated with the higher fitness between two individuals determined by a mutual learning factor. Moreover, a sequential decision-making process investigates the imbalanced classification issue, in which a convolutional neural network (CNN) is used as the foundation for policy architecture. At first, initial weights are produced using the ML-ABC algorithm. After that, the agent receives a sample at each phase and classifies it, obtaining environmental rewards. The minority class receives more rewards than the majority class. Eventually, the agent discovers an ideal strategy with the aid of a specific reward function and a beneficial learning environment. We evaluate our proposed approach on the Z-Alizadeh Sani myocarditis dataset based on standard criteria and demonstrate that the proposed method gives superior myocarditis diagnosis performance.