A comparative study of different pre-trained deep learning models and custom CNN for pancreatic tumor detection

M. Zavalsiz, Sleiman Alhajj, Kashfia Sailunaz, Tansel Özyer, Reda Alhajj
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引用次数: 1

Abstract

Artificial Intelligence and its sub-branches like Machine Learning (ML) and Deep Learning (DL) applications have the potential to have positive effects that can directly affect human life. Medical imaging is briefly making the internal structure of the human body visible with various methods. With deep learning models, cancer detection, which is one of the most lethal diseases in the world, can be made possible with high accuracy. Pancreatic Tumor detection, which is one of the cancer types with the highest fatality rate, is one of the main targets of this project, together with the data set of Computed Tomography images, which is one of the medical imaging techniques and has an effective structure in Pancreatic Cancer imaging. In the field of image classification, which is a computer vision task, the transfer learning technique, which has gained popularity in recent years, has been applied quite frequently. Using pre-trained models were previously trained on a fairly large dataset and using them on medical images is common nowadays. The main objective of this article is to use this method, which is very popular in the medical imaging field, in the detection of PDAC, one of the deadliest types of pancreatic cancer, and to investigate how it per- forms compared to the custom model created and trained from scratch. The pre-trained models which are used in this project are VGG-16 and ResNet, which are popular Convolutional Neutral Network models, for Pancreatic Tumor Detection task. With the use of these models, early diagnosis of pancreatic cancer, which progresses insidiously and therefore does not spread to neighboring tissues and organs when the treatment process is started, may be possible. Due to the abundance of medical images reviewed by medical professionals, which is one of the main causes for heavy workload of healthcare systems, this application can assist radiologists and other specialists in Pancreatic Tumor detection by providing faster and more accurate method
不同预训练深度学习模型与自定义CNN用于胰腺肿瘤检测的比较研究
人工智能及其分支,如机器学习(ML)和深度学习(DL)应用程序,有可能产生直接影响人类生活的积极影响。简言之,医学成像就是用各种方法使人体的内部结构可见。有了深度学习模型,世界上最致命的疾病之一癌症的检测就可以以高精度成为可能。胰腺肿瘤检测是致死率最高的癌症类型之一,是本项目的主要目标之一,与计算机断层扫描图像数据集一起,是医学成像技术之一,在胰腺癌成像中具有有效的结构。在图像分类这一计算机视觉任务中,近年来兴起的迁移学习技术得到了相当广泛的应用。使用预训练模型之前是在一个相当大的数据集上训练的,现在在医学图像上使用它们是很常见的。本文的主要目的是使用这种在医学成像领域非常流行的方法来检测最致命的胰腺癌之一PDAC,并研究它与从头创建和训练的自定义模型相比的表现。本项目使用的预训练模型是VGG-16和ResNet,这两种流行的卷积神经网络模型,用于胰腺肿瘤检测任务。随着这些模型的使用,胰腺癌的早期诊断可能成为可能,胰腺癌的发展是隐性的,因此在治疗过程开始时不会扩散到邻近的组织和器官。由于医学专业人员需要审查大量的医学图像,这是医疗保健系统工作量大的主要原因之一,因此该应用程序可以通过提供更快,更准确的方法来协助放射科医生和其他专家进行胰腺肿瘤检测
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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