Deep learning ensemble 2D CNN approach towards the detection of lung cancer
Abstract In recent times, deep learning has emerged as a great resource to help research in medical sciences. A lot of work has been done with the help of computer science to expose and predict different diseases in human beings. This research uses the Deep Learning algorithm Convolutional Neural Ne...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-02-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-29656-z |
| _version_ | 1797864616209940480 |
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| author | Asghar Ali Shah Hafiz Abid Mahmood Malik AbdulHafeez Muhammad Abdullah Alourani Zaeem Arif Butt |
| author_facet | Asghar Ali Shah Hafiz Abid Mahmood Malik AbdulHafeez Muhammad Abdullah Alourani Zaeem Arif Butt |
| author_sort | Asghar Ali Shah |
| collection | DOAJ |
| description | Abstract In recent times, deep learning has emerged as a great resource to help research in medical sciences. A lot of work has been done with the help of computer science to expose and predict different diseases in human beings. This research uses the Deep Learning algorithm Convolutional Neural Network (CNN) to detect a Lung Nodule, which can be cancerous, from different CT Scan images given to the model. For this work, an Ensemble approach has been developed to address the issue of Lung Nodule Detection. Instead of using only one Deep Learning model, we combined the performance of two or more CNNs so they could perform and predict the outcome with more accuracy. The LUNA 16 Grand challenge dataset has been utilized, which is available online on their website. The dataset consists of a CT scan with annotations that better understand the data and information about each CT scan. Deep Learning works the same way our brain neurons work; therefore, deep learning is based on Artificial Neural Networks. An extensive CT scan dataset is collected to train the deep learning model. CNNs are prepared using the data set to classify cancerous and non-cancerous images. A set of training, validation, and testing datasets is developed, which is used by our Deep Ensemble 2D CNN. Deep Ensemble 2D CNN consists of three different CNNs with different layers, kernels, and pooling techniques. Our Deep Ensemble 2D CNN gave us a great result with 95% combined accuracy, which is higher than the baseline method. |
| first_indexed | 2024-04-09T22:55:55Z |
| format | Article |
| id | doaj.art-367a7ec4b4944b24a2a08ab327929a68 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-09T22:55:55Z |
| publishDate | 2023-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-367a7ec4b4944b24a2a08ab327929a682023-03-22T11:16:24ZengNature PortfolioScientific Reports2045-23222023-02-0113111510.1038/s41598-023-29656-zDeep learning ensemble 2D CNN approach towards the detection of lung cancerAsghar Ali Shah0Hafiz Abid Mahmood Malik1AbdulHafeez Muhammad2Abdullah Alourani3Zaeem Arif Butt4Department of Computer Sciences, Bahria UniversityFaculty of Computer Studies, Arab Open University BahrainDepartment of Computer Sciences, Bahria UniversityDepartment of Computer Science and Information, College of Science in Zulfi, Majmaah UniversityDepartment of Computer Sciences, Bahria UniversityAbstract In recent times, deep learning has emerged as a great resource to help research in medical sciences. A lot of work has been done with the help of computer science to expose and predict different diseases in human beings. This research uses the Deep Learning algorithm Convolutional Neural Network (CNN) to detect a Lung Nodule, which can be cancerous, from different CT Scan images given to the model. For this work, an Ensemble approach has been developed to address the issue of Lung Nodule Detection. Instead of using only one Deep Learning model, we combined the performance of two or more CNNs so they could perform and predict the outcome with more accuracy. The LUNA 16 Grand challenge dataset has been utilized, which is available online on their website. The dataset consists of a CT scan with annotations that better understand the data and information about each CT scan. Deep Learning works the same way our brain neurons work; therefore, deep learning is based on Artificial Neural Networks. An extensive CT scan dataset is collected to train the deep learning model. CNNs are prepared using the data set to classify cancerous and non-cancerous images. A set of training, validation, and testing datasets is developed, which is used by our Deep Ensemble 2D CNN. Deep Ensemble 2D CNN consists of three different CNNs with different layers, kernels, and pooling techniques. Our Deep Ensemble 2D CNN gave us a great result with 95% combined accuracy, which is higher than the baseline method.https://doi.org/10.1038/s41598-023-29656-z |
| spellingShingle | Asghar Ali Shah Hafiz Abid Mahmood Malik AbdulHafeez Muhammad Abdullah Alourani Zaeem Arif Butt Deep learning ensemble 2D CNN approach towards the detection of lung cancer Scientific Reports |
| title | Deep learning ensemble 2D CNN approach towards the detection of lung cancer |
| title_full | Deep learning ensemble 2D CNN approach towards the detection of lung cancer |
| title_fullStr | Deep learning ensemble 2D CNN approach towards the detection of lung cancer |
| title_full_unstemmed | Deep learning ensemble 2D CNN approach towards the detection of lung cancer |
| title_short | Deep learning ensemble 2D CNN approach towards the detection of lung cancer |
| title_sort | deep learning ensemble 2d cnn approach towards the detection of lung cancer |
| url | https://doi.org/10.1038/s41598-023-29656-z |
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