Abstract
A large number of images that are usually registered images in a training dataset are required for creating classification models because training of images using a convolutional neural network is done using supervised learning. It takes a significant amount of time and effort to create a registered dataset because recently computed tomography (CT) and magnetic resonance imaging devices produce hundreds of images per examination. This study aims to evaluate the overall accuracy of the additional learning and automatic classification systems for CT images. The study involved 700 patients, who were subjected to contrast or non-contrast CT examination of brain, neck, chest, abdomen, or pelvis. The images were divided into 500 images per class. The 10-class dataset was prepared with 10 datasets including with 5000–50,000 images. The overall accuracy was calculated using a confusion matrix for evaluating the created models. The highest overall reference accuracy was 0.9033 when the model was trained with a dataset containing 50,000 images. The additional learning for manual training was effective when datasets with a large number of images were used. The additional learning for automatic training requires models with an inherent higher accuracy for the classification.
Highlights
Deep learning techniques [1,2,3], including deep convolutional neural networks (CNNs), are being employed widely in the field of image processing to conduct image classification [4,5,6], object detection [7,8], and image segmentation [9,10,11,12] tasks
We focus on additional learning and automatic learning for computed tomography (CT) images because a current CT scanner has the ability to generate a large number of images per examination
This study aims to evaluate the overall accuracy of the additional learning and the automatic classification systems for
Summary
Deep learning techniques [1,2,3], including deep convolutional neural networks (CNNs), are being employed widely in the field of image processing to conduct image classification [4,5,6], object detection [7,8], and image segmentation [9,10,11,12] tasks. Many studies [4,5,6,7,8,9,10,11,12,13,14,15,16,17] have investigated the applications of deep learning techniques in medical imaging, which serve as an expansion to this field. Image diagnosis using computed tomography (CT) and magnetic resonance imaging (MRI) is currently becoming indispensable in the medical field. A large number of CT and MRI images are being generated from daily medical examinations, these images are referred to as a follow-up for only a few specific patients. There are many existing models [4,5,6,7,13] for the classification of medical images; these models are not usually updated since they are created only when needed.
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