An Investigation in Analyzing the Food Quality Well-Being for Lung Cancer Using Blockchain through CNN
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Authors
Aboamer, Mohamed AbdelkaderSikkandar, Mohamed Yacin
Gupta, Sachin
Vives, Luis
Joshi, Kapil
Omarov, Batyrkhan
Singh, Sitesh Kumar
Issue Date
2022-01-01Keywords
Biological organsConvolutional neural networks
Deep learning
Diseases
Forecasting
Image enhancement
Pixels
Regression analysis
Metadata
Show full item recordPublisher
Hindawi LimitedJournal
Journal of Food QualityDOI
10.1155/2022/5845870Additional Links
https://www.hindawi.com/journals/jfq/2022/5845870/Abstract
Deep learning (DL) is a new approach that provides exceptional speed in healthcare activities with greater accuracy. In this regard, "convolutional neural network"or CNN and blockchain are two important parts that together fasten the disease detection procedures securely. CNN can detect and predict diseases like lung cancer and help determine food quality, and blockchain is responsible for data. This research is going to analyze the extension of blockchain with the help of CNN for lung cancer prediction and making food safer. CNN algorithm has been trained with a huge number of images by altering the filters, features, epoch values, padding value, kernel size, and resolution. Subsequently, the CNN accuracy has been measured to understand how these factors affect the accuracy. A linear regression analysis has been carried out in IBM SPSS where the independent variables selected are image dataset augmentation, epochs, features, pixel size (90 × 90 to 512 × 512), kernel size (0-7), filters (10-40), and padding. The dependent variable is the accuracy of CNN. Findings suggested that a larger number of epochs improve the CNN accuracy; however, when more than 12 epochs are considered, the accuracy may decrease. A greater pixel/resolution also improves the accuracy of cancer and food image detection. When images are provided with excellent features and filters, the CNN accuracy improves. The main objective of this research is to comprehend how the independent variables affect the accuracy (dependent), but the reading may not be fully exact, and thus, the researcher has conceded out a minor task, which delivered evidence supportive of the analysis and against the analysis. As a result, it can be determined that image augmentation and a large number of images develop the CNN accuracy in lung cancer prediction and food safety determination when features and filters are applied correctly. A total of 10-12 epochs are desirable for CNN to receive 99% accuracy with 1 padding.Type
info:eu-repo/semantics/articleRights
info:eu-repo/semantics/openAccessAttribution-NonCommercial-ShareAlike 4.0 International
Language
engISSN
01469428EISSN
17454557ae974a485f413a2113503eed53cd6c53
10.1155/2022/5845870
Scopus Count
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The following license files are associated with this item:
- Creative Commons