Bekheet Mohamed, Sallah Mohammed, Alghamdi Norah S, Rusu-Both Roxana, Elgarayhi Ahmed, Elmogy Mohammed
Applied Mathematical Physics Research Group, Physics Department, Faculty of Science, Mansoura University, Mansoura 35516, Egypt.
Radiography and Medical Imaging Department, Faculty of Applied Health Sciences Technology, Sphinx University, New Assiut 71515, Egypt.
Diagnostics (Basel). 2024 Jan 24;14(3):255. doi: 10.3390/diagnostics14030255.
Ischemic heart condition is one of the most prevalent causes of death that can be treated more effectively and lead to fewer fatalities if identified early. Heart muscle fibrosis affects the diastolic and systolic function of the heart and is linked to unfavorable cardiovascular outcomes. Cardiac magnetic resonance (CMR) scarring, a risk factor for ischemic heart disease, may be accurately identified by magnetic resonance imaging (MRI) to recognize fibrosis. In the past few decades, numerous methods based on MRI have been employed to identify and categorize cardiac fibrosis. Because they increase the therapeutic advantages and the likelihood that patients will survive, developing these approaches is essential and has significant medical benefits. A brand-new method that uses MRI has been suggested to help with diagnosing. Advances in deep learning (DL) networks contribute to the early and accurate diagnosis of heart muscle fibrosis. This study introduces a new deep network known as FibrosisNet, which detects and classifies fibrosis if it is present. It includes some of 17 various series layers to achieve the fibrosis detection target. The introduced classification system is trained and evaluated for the best performance results. In addition, deep transfer-learning models are applied to the different famous convolution neural networks to find fibrosis detection architectures. The FibrosisNet architecture achieves an accuracy of 96.05%, a sensitivity of 97.56%, and an F1-Score of 96.54%. The experimental results show that FibrosisNet has numerous benefits and produces higher results than current state-of-the-art methods and other advanced CNN approaches.
缺血性心脏病是最常见的死亡原因之一，如果能早期发现，可得到更有效的治疗，死亡率也会更低。心肌纤维化会影响心脏的舒张和收缩功能，并与不良心血管结局相关。心脏磁共振（CMR）瘢痕是缺血性心脏病的一个危险因素，可通过磁共振成像（MRI）准确识别以检测纤维化。在过去几十年中，已采用了多种基于MRI的方法来识别和分类心脏纤维化。开发这些方法至关重要，因为它们能增加治疗优势并提高患者存活的可能性，具有重大的医学益处。有人提出了一种全新的利用MRI的方法来辅助诊断。深度学习（DL）网络的进展有助于心肌纤维化的早期准确诊断。本研究引入了一种名为FibrosisNet的新型深度网络，可检测并对存在的纤维化进行分类。它包含17个不同系列层中的一些层以实现纤维化检测目标。对引入的分类系统进行训练和评估以获得最佳性能结果。此外，将深度迁移学习模型应用于不同的著名卷积神经网络以寻找纤维化检测架构。FibrosisNet架构的准确率为96.05%，灵敏度为97.56%，F1分数为96.54%。实验结果表明，FibrosisNet有诸多优点，且比当前最先进的方法和其他先进的卷积神经网络方法产生的结果更高。
