Mostrar el registro sencillo del ítem

dc.contributor.authorYang, Jing
dc.contributor.authorSadiq, Touseef
dc.contributor.authorXiong, Jiale
dc.contributor.authorAwais, Muhammad
dc.contributor.authorBhatti, Uzair Aslam
dc.contributor.authorAlizadehsani, Roohallah
dc.contributor.authorGorriz Sáez, Juan Manuel 
dc.date.accessioned2024-06-18T13:12:08Z
dc.date.available2024-06-18T13:12:08Z
dc.date.issued2024-02-15
dc.identifier.citationYang, J., et al.: A novel myocarditis detection combining deep reinforcement learning and an improved differential evolution algorithm. CAAI Trans. Intell. Technol. 1–14 (2024). https://doi.org/10.1049/cit2.12289es_ES
dc.identifier.urihttps://hdl.handle.net/10481/92685
dc.description.abstractMyocarditis 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.es_ES
dc.language.isoenges_ES
dc.publisherJohn Wiley & Sonses_ES
dc.rightsAtribución 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subject2-Des_ES
dc.subjectArtificial intelligence es_ES
dc.titleA novel myocarditis detection combining deep reinforcement learning and an improved differential evolution algorithmes_ES
dc.typejournal articlees_ES
dc.rights.accessRightsopen accesses_ES
dc.identifier.doi10.1049/cit2.12289
dc.type.hasVersionVoRes_ES


Ficheros en el ítem

[PDF]

Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem

Atribución 4.0 Internacional
Excepto si se señala otra cosa, la licencia del ítem se describe como Atribución 4.0 Internacional