Detection and classification of lung cancer using CNN and Google net
Lung cancer is a serious disease occurring in human being. Medical treatment process mainly depends on cancer types and its location. It is possible to save many precious human lives by detecting cancer cells as early as possible. Developing an automated tool is essential to detecting malignant stat...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-12-01
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| Series: | Measurement: Sensors |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2665917422002227 |
| _version_ | 1811214356035141632 |
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| author | R. Pandian V. Vedanarayanan D.N.S. Ravi Kumar R. Rajakumar |
| author_facet | R. Pandian V. Vedanarayanan D.N.S. Ravi Kumar R. Rajakumar |
| author_sort | R. Pandian |
| collection | DOAJ |
| description | Lung cancer is a serious disease occurring in human being. Medical treatment process mainly depends on cancer types and its location. It is possible to save many precious human lives by detecting cancer cells as early as possible. Developing an automated tool is essential to detecting malignant states at the earliest possible stage. The accuracy of prediction has always been a challenge, despite the many algorithms proposed in the past by many researchers. Using artificial neural networks, this study proposes a methodology to detect abnormal lung tissue growth. In order to achieve great accuracy, a tool with a higher probability of detection is taken into account. The manual interpretation of results is incapable of avoiding misdiagnoses. During the course of this research, lung images from both healthy and malignant individuals were analyzed. Data bases have also been developed for the various views of the CT scanning system, such as axial, coronal, and sagittal. A neural network, based on the textural characteristics of the images, makes it feasible for classification of the normal images, identifying away from the malignant ones. In order to overcome this problem, CNN and Google Net deep learning algorithms have been proposed to detect Cancer. Both the region proposal network and the classifier network use the VGG-16 architecture as their base layer. The algorithm achieves a precision of 98% in detection and classification. Based on confusion matrix computation and classification accuracy results, a quantitative analysis of the proposed network has been conducted. |
| first_indexed | 2024-04-12T06:03:07Z |
| format | Article |
| id | doaj.art-b0e37ba0779e406899ed97c22b9629e0 |
| institution | Directory Open Access Journal |
| issn | 2665-9174 |
| language | English |
| last_indexed | 2024-04-12T06:03:07Z |
| publishDate | 2022-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Measurement: Sensors |
| spelling | doaj.art-b0e37ba0779e406899ed97c22b9629e02022-12-22T03:44:59ZengElsevierMeasurement: Sensors2665-91742022-12-0124100588Detection and classification of lung cancer using CNN and Google netR. Pandian0V. Vedanarayanan1D.N.S. Ravi Kumar2R. Rajakumar3Corresponding author.; School of EEE, Sathyabama Institute of Science and Technology, IndiaSchool of EEE, Sathyabama Institute of Science and Technology, IndiaSchool of EEE, Sathyabama Institute of Science and Technology, IndiaSchool of EEE, Sathyabama Institute of Science and Technology, IndiaLung cancer is a serious disease occurring in human being. Medical treatment process mainly depends on cancer types and its location. It is possible to save many precious human lives by detecting cancer cells as early as possible. Developing an automated tool is essential to detecting malignant states at the earliest possible stage. The accuracy of prediction has always been a challenge, despite the many algorithms proposed in the past by many researchers. Using artificial neural networks, this study proposes a methodology to detect abnormal lung tissue growth. In order to achieve great accuracy, a tool with a higher probability of detection is taken into account. The manual interpretation of results is incapable of avoiding misdiagnoses. During the course of this research, lung images from both healthy and malignant individuals were analyzed. Data bases have also been developed for the various views of the CT scanning system, such as axial, coronal, and sagittal. A neural network, based on the textural characteristics of the images, makes it feasible for classification of the normal images, identifying away from the malignant ones. In order to overcome this problem, CNN and Google Net deep learning algorithms have been proposed to detect Cancer. Both the region proposal network and the classifier network use the VGG-16 architecture as their base layer. The algorithm achieves a precision of 98% in detection and classification. Based on confusion matrix computation and classification accuracy results, a quantitative analysis of the proposed network has been conducted.http://www.sciencedirect.com/science/article/pii/S2665917422002227CancerCNNGoogle netVGG-16,CTAccuracyImage |
| spellingShingle | R. Pandian V. Vedanarayanan D.N.S. Ravi Kumar R. Rajakumar Detection and classification of lung cancer using CNN and Google net Measurement: Sensors Cancer CNN Google net VGG-16,CT Accuracy Image |
| title | Detection and classification of lung cancer using CNN and Google net |
| title_full | Detection and classification of lung cancer using CNN and Google net |
| title_fullStr | Detection and classification of lung cancer using CNN and Google net |
| title_full_unstemmed | Detection and classification of lung cancer using CNN and Google net |
| title_short | Detection and classification of lung cancer using CNN and Google net |
| title_sort | detection and classification of lung cancer using cnn and google net |
| topic | Cancer CNN Google net VGG-16,CT Accuracy Image |
| url | http://www.sciencedirect.com/science/article/pii/S2665917422002227 |
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