Designation of thorax and non-thorax regions for lung cancer detection in CT scan images using deep learning / Mohd Firdaus Abdullah … [et al.]
Lung cancer is a common cause of death among people throughout the world. Lung cancer detection can be done in several ways, such as radiography, magnetic resonance imaging (MRI) and computed tomography (CT). These methods take up a lot of resources in terms of time and money. However, CT has good f...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Universiti Teknologi MARA Press (Penerbit UiTM)
2020
|
| Subjects: | |
| Online Access: | http://ir.uitm.edu.my/id/eprint/42382/1/42382.pdf http://ir.uitm.edu.my/id/eprint/42382/ https://jeesr.uitm.edu.my https://doi.org/10.24191/jeesr.v17i1.006 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Lung cancer is a common cause of death among people throughout the world. Lung cancer detection can be done in several ways, such as radiography, magnetic resonance imaging (MRI) and computed tomography (CT). These methods take up a lot of resources in terms of time and money. However, CT has good for lung cancer detection, offers a lower cost, short imaging time and widespread availability. Early diagnosis of lung cancer can help doctors to treat patients in order to reduce the number of mortalities. This paper presents designation of thorax and nonthorax regions for lung cancer detection in CT Scan images using deep learning. The primary aim of this research is to propose an intelligent, fast and accurate method for lung cancer detection. As initial stage we proposed a thorax and non-thorax slice detection for CT scan images using deep convolutional neural network (DCNN) so that later it can be used to simplify the process of lung cancer detection. The proposed method involved the development of DCNN network architecture. It comprises the following steps which involves designed the convolution layer, activation function, max pooling, fully-connected layer and output size. We present three DCNN structures to find the most effective network for thorax and non-thorax region detection. All networks were trained using 12866 images and validate the performance using 5514 images. Simulation results showed that DCNN 2 and DCNN 3 were able to classify the thorax and non-thorax regions with good performance. The most efficient network is the DCNN with fivelayer structure (DCNN 2). This DCNN model achieved an accuracy of 99.42% with moderate duration of training time. |
|---|