A back-propagation convolutional neural network achieved a 3.4% error rate on the full test set, and a 1% error rate with a 9% reject rate on handwritten zipcode digits.
Does a Convolutional Neural Network-Clustering (CNN-KCL) model accurately diagnose myocarditis in suspected patients?
A novel deep learning-based model (CNN-KCL) demonstrated high diagnostic accuracy (97.41%) for myocarditis, highlighting the potential of artificial intelligence to assist in complex cardiac imaging interpretations.
Myocarditis is the form of an inflammation of the middle layer of the heart wall which is caused by a viral infection and can affect the heart muscle and its electrical system. It has remained one of the most challenging diagnoses in cardiology. Myocardial is the prime cause of unexpected death in approximately 20% of adults less than 40 years of age. Cardiac MRI (CMR) has been considered a noninvasive and golden standard diagnostic tool for suspected myocarditis and plays an indispensable role in diagnosing various cardiac diseases. However, the performance of CMR depends heavily on the clinical presentation and features such as chest pain, arrhythmia, and heart failure. Besides, other imaging factors like artifacts, technical errors, pulse sequence, acquisition parameters, contrast agent dose, and more importantly qualitatively visual interpretation can affect the result of the diagnosis. This paper introduces a new deep learning-based model called Convolutional Neural Network-Clustering (CNN-KCL) to diagnose Myocarditis. In this study, we used 47 subjects with a total number of 98,898 images to diagnose myocarditis disease. Our results demonstrate that the proposed method achieves an accuracy of 97.41% based on 10 fold-cross validation technique with 4 clusters for diagnosis of Myocarditis. To the best of our knowledge, this research is the first to use deep learning algorithms for the diagnosis of myocarditis.
Sharifrazi et al. (Sat,) conducted a other in Handwritten digit recognition (n=12,647). Back-propagation convolutional neural network was evaluated on Error rate on test set. A back-propagation convolutional neural network achieved a 3.4% error rate on the full test set, and a 1% error rate with a 9% reject rate on handwritten zipcode digits.