An intelligent CAD system for automated detection of thorax region in CT scan of lung cancer / 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...
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| Format: | Book |
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2020.
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| LEADER | 00000 am a22000003u 4500 | ||
|---|---|---|---|
| 001 | repouitm_69745 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Abdullah, Mohd Firdaus |e author |
| 700 | 1 | 0 | |a Sulaiman, Siti Noraini |e author |
| 700 | 1 | 0 | |a Osman, Muhammad Khusairi |e author |
| 700 | 1 | 0 | |a A. Karim, Noor Khairiah |e author |
| 700 | 1 | 0 | |a Isa, Iza Sazanita |e author |
| 245 | 0 | 0 | |a An intelligent CAD system for automated detection of thorax region in CT scan of lung cancer / Mohd Firdaus Abdullah ... [et al.] |
| 260 | |c 2020. | ||
| 500 | |a https://ir.uitm.edu.my/id/eprint/69745/1/69745.pdf | ||
| 520 | |a 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 project presents an intelligent CAD system for automated detection of thorax region in CT scan of lung cancer. The primary aim of this research is to propose an intelligent, fast and accurate method for 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 Deep Convolutional Neural Network were able to classify the thorax and non-thorax regions with good performance with an accuracy of 99.42%. This may be considered a promising aspect in realizing an intelligent, fast and accurate method for lung cancer detection. | ||
| 546 | |a en | ||
| 690 | |a Medical technology | ||
| 690 | |a Computer applications to medicine. Medical informatics | ||
| 690 | |a T Technology (General) | ||
| 690 | |a Technological innovations | ||
| 655 | 7 | |a Conference or Workshop Item |2 local | |
| 655 | 7 | |a PeerReviewed |2 local | |
| 787 | 0 | |n https://ir.uitm.edu.my/id/eprint/69745/ | |
| 856 | 4 | 1 | |u https://ir.uitm.edu.my/id/eprint/69745/ |z Link Metadata |