Role of Artificial Intelligence and Deep Learning in Easier Skin Cancer Detection through Antioxidants Present in Food
Skin cancer is one of the most common types of cancer that has a high mortality rate. Majorly, two types of skin cancer are the most common, which are melanoma and nonmelanoma skin cancer. Each year, approximately 55% of individuals die due to skin cancer. Early detection of skin cancer enhances the...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Journal of Food Quality |
| Online Access: | http://dx.doi.org/10.1155/2022/5890666 |
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| author | Sreevidya R. C. Jalaja G Sajitha N D. Lakshmi Padmaja S. Nagaprasad Kumud Pant Yekula Prasanna Kumar |
| author_facet | Sreevidya R. C. Jalaja G Sajitha N D. Lakshmi Padmaja S. Nagaprasad Kumud Pant Yekula Prasanna Kumar |
| author_sort | Sreevidya R. C. |
| collection | DOAJ |
| description | Skin cancer is one of the most common types of cancer that has a high mortality rate. Majorly, two types of skin cancer are the most common, which are melanoma and nonmelanoma skin cancer. Each year, approximately 55% of individuals die due to skin cancer. Early detection of skin cancer enhances the survival rate of individuals. There are various antioxidants like vitamins C, E, and A, zinc, and selenium present in various foods that can be helpful in preventing skin cancer. “Deep Learning” (DL) is an effective method to detect cancerous lesions. The study’s purpose is to comprehend the vital function performed by DL methods in supporting healthcare professionals in easier skin cancer detection using big data networks. The present research analyzes the accuracy, sensitivity, and specificity of “Convolutional Neural Network” (CNN) for DL in the early detection of skin cancer. A statistical analysis has been done with IBM SPSS software to understand how the accuracy, sensitivity, and specificity of CNN change with the change in image number, augmentation number, epochs, and resolution of images. These factors have been considered independent variables, and accuracy, sensitivity, and specificity have been considered the dependent variables. After that, a linear regression analysis was carried out to obtain t and p values. The major scope of the study is to analyze the major role played by the DL models through the big data network in the medical industry. The researchers also found that when additional characteristics are present, image resolution does not have the potential to reduce image accuracy, specificity, or sensitivity. The scope of the study is more focused on using a DL-based big data network for supporting healthcare workers in detecting skin cancer at an early stage and the role of technology in supporting medical practitioners in rendering better treatment. Findings showed that the number of training images increases the accuracy, sensitivity, and specificity of CNN architecture when various and effective augmentation techniques are used. Image resolution did not show any significant relationship with accuracy. The number of epochs positively affected the accuracy, sensitivity, and specificity; however, more than 98% accuracy has been observed with epochs between 50 and 70. |
| format | Article |
| id | doaj-art-c09dda3482f9440391ade91bec04cd90 |
| institution | OA Journals |
| issn | 1745-4557 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Food Quality |
| spelling | doaj-art-c09dda3482f9440391ade91bec04cd902025-08-20T02:19:22ZengWileyJournal of Food Quality1745-45572022-01-01202210.1155/2022/5890666Role of Artificial Intelligence and Deep Learning in Easier Skin Cancer Detection through Antioxidants Present in FoodSreevidya R. C.0Jalaja G1Sajitha N2D. Lakshmi Padmaja3S. Nagaprasad4Kumud Pant5Yekula Prasanna Kumar6Department of CSEDepartment of CSEDepartment of AIMLDepartment of Information TechnologyTara Government College (A)Department of BiotechnologyDepartment of Mining EngineeringSkin cancer is one of the most common types of cancer that has a high mortality rate. Majorly, two types of skin cancer are the most common, which are melanoma and nonmelanoma skin cancer. Each year, approximately 55% of individuals die due to skin cancer. Early detection of skin cancer enhances the survival rate of individuals. There are various antioxidants like vitamins C, E, and A, zinc, and selenium present in various foods that can be helpful in preventing skin cancer. “Deep Learning” (DL) is an effective method to detect cancerous lesions. The study’s purpose is to comprehend the vital function performed by DL methods in supporting healthcare professionals in easier skin cancer detection using big data networks. The present research analyzes the accuracy, sensitivity, and specificity of “Convolutional Neural Network” (CNN) for DL in the early detection of skin cancer. A statistical analysis has been done with IBM SPSS software to understand how the accuracy, sensitivity, and specificity of CNN change with the change in image number, augmentation number, epochs, and resolution of images. These factors have been considered independent variables, and accuracy, sensitivity, and specificity have been considered the dependent variables. After that, a linear regression analysis was carried out to obtain t and p values. The major scope of the study is to analyze the major role played by the DL models through the big data network in the medical industry. The researchers also found that when additional characteristics are present, image resolution does not have the potential to reduce image accuracy, specificity, or sensitivity. The scope of the study is more focused on using a DL-based big data network for supporting healthcare workers in detecting skin cancer at an early stage and the role of technology in supporting medical practitioners in rendering better treatment. Findings showed that the number of training images increases the accuracy, sensitivity, and specificity of CNN architecture when various and effective augmentation techniques are used. Image resolution did not show any significant relationship with accuracy. The number of epochs positively affected the accuracy, sensitivity, and specificity; however, more than 98% accuracy has been observed with epochs between 50 and 70.http://dx.doi.org/10.1155/2022/5890666 |
| spellingShingle | Sreevidya R. C. Jalaja G Sajitha N D. Lakshmi Padmaja S. Nagaprasad Kumud Pant Yekula Prasanna Kumar Role of Artificial Intelligence and Deep Learning in Easier Skin Cancer Detection through Antioxidants Present in Food Journal of Food Quality |
| title | Role of Artificial Intelligence and Deep Learning in Easier Skin Cancer Detection through Antioxidants Present in Food |
| title_full | Role of Artificial Intelligence and Deep Learning in Easier Skin Cancer Detection through Antioxidants Present in Food |
| title_fullStr | Role of Artificial Intelligence and Deep Learning in Easier Skin Cancer Detection through Antioxidants Present in Food |
| title_full_unstemmed | Role of Artificial Intelligence and Deep Learning in Easier Skin Cancer Detection through Antioxidants Present in Food |
| title_short | Role of Artificial Intelligence and Deep Learning in Easier Skin Cancer Detection through Antioxidants Present in Food |
| title_sort | role of artificial intelligence and deep learning in easier skin cancer detection through antioxidants present in food |
| url | http://dx.doi.org/10.1155/2022/5890666 |
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