Volume 32, Issue 1 (April 2021)                   Studies in Medical Sciences 2021, 32(1): 67-81 | Back to browse issues page

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Majidpourkhoei R, Alilou M, Majidzadeh K, BabazadehSangar A. INTRODUCING AN INTELLIGENT FRAMEWORK FOR DETECTION OF SUSPECTED LUNG NODULES. Studies in Medical Sciences 2021; 32 (1) :67-81
URL: http://umj.umsu.ac.ir/article-1-5385-en.html
Assistant Professor, Department of Computer engineering, Urmia Branch, Islamic Azad University, Urmia, Iran. (Corresponding Author) , Mehdi.a.m.i.p@gmail.com
Abstract:   (1749 Views)
Background & Aims: One of the symptoms of lung cancer, which is one of the deadliest cancers, is the lung nodules. It is very difficult to detect these tiny nodules on CT scans of the lungs with the naked eye. Therefore, intelligent systems or computer-aided detection (CAD) systems can assist a radiologist in detecting, locating, and evaluating the quality of lung nodules. The most important challenge of existing intelligent systems is the balanced improvement of accuracy, sensitivity, specificity, and reduction of false positive rate (FPr), and also the complexity of these systems has reduced the efficiency and speed of execution. Therefore, the purpose of this study was to provide an agile framework and optimize the challenge.
Materials & Methods: One of the new subfields of artificial intelligence is the deep learning and orientation of CNN networks, which has been widely used in the analysis of medical images in recent years. In this research, an innovative network based on CNN networks of LeNet type is proposed to extract image features as well as image classification. The used dataset is a subset of 7072 image pieces derived from the LIDC-IDRI standard dataset. The size of nodules of these images, which are used to train and validate the network, are 1 to 4 mm.
Results: The training and validation processes of this network were performed with a computer device (configurations 2.4GHz Core i5 processor, 8GB of memory, and Intel Graphics 520) in five hours and eleven minutes and the accuracy, sensitivity, and specificity are 91.1%, 85.3% and 92.8%, respectively.
Conclusion: Based on the standard basis of the proposed model and also the use of valid database images to measure the network and compare with previous works, the results establish a good balance between evaluation criteria, and with faster implementation gain the necessary capability for real time applications.
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Type of Study: Research | Subject: General

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