Epoch and accuracy based empirical study for cardiac MRI segmentation using deep learning technique

Abstract

Cardiac magnetic resonance imaging (CMRI) is a non-invasive imaging technique to analyse the structure and function of the heart. It was enhanced considerably over several years to deliver functional information for diagnosing and managing cardiovascular disease. CMRI image delivers non-invasive, clear access to the heart and great vessels. The segmentation of CMRI provides quantification parameters such as myocardial viability, ejection fraction, cardiac chamber volume, and morphological details. In general, experts interpret the CMR images by delineating the images manually. The manual segmentation process is time-consuming, and it has been observed that the final observation varied with the opinion of the different experts. Convolution neural network is a new-age technology that provides impressive results compared to manual ones. In this study convolution neural network model is used for the segmentation task. The neural network parameters have been optimized to perform on the novel data set for accurate predictions. With other parameters, epochs play an essential role in training the network, as the network should not be under-fitted or over-fitted. The relationship between the hyperparameter epoch and accuracy is established in the model. The model delivers the accuracy of 0.88 in terms of the IoU coefficient.

Teachable Machine: Optimization of Herbal Plant Image Classification Based on Epoch Value, Batch Size and Learning Rate

“…To prove the use of the three parameter values above (epoch, batch size and learning rate) researchers have summarized from various sources for the minimum and maximum values used in object detection testing, but cannot be separated from the influence of image quality, the number of datasets and algorithms used. For epoch values ranging from 10; 50; 100; 250; 750 and 1000 epochs that have been done by previous researchers [60], [61], [62], then for batch sizes of 16; 32; 64; 128; 256 and 512 available on Teachable Machine, these values are the minimum and maximum values that are already available without the need for modification. Then, for the learning rate value based on the average of previous research ranges from 0.00001; 0.0001; 0.001; 0.01; 0.1 and 1 [63], [64], [65].…”