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A Deep Learning Strategy for Predicting Liver Cancer Using Convolutional Neural Network Algorithm


Affiliations
1 Research Scholar, Department of Computer Science, Hemchandracharya North Gujarat University, Patan-384265, Gujarat, India
2 Assistant Professor, Department of Computer Science, Hemchandracharya North Gujarat University, Patan-384265, Gujarat, India

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One of the common types of cancer is liver cancer, early detection and diagnosis of which are critical. Discovery, decision, and aggressive therapy can prevent most cancer deaths. We use data mining approaches (Convolutional Neural Networks) to build prediction models for liver cancer with the most widely used statistical analysis methodology. Around 579 records and 10 variables were included in the data collection. The model was built, evaluated, and compared using a k-fold cross-validation process. CNN was the best accurate predictor for this domain with a test set accuracy of 100%.


Keywords

Cancer, Convolutional Neural Networks, data mining, statistical analysis.
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  • A Deep Learning Strategy for Predicting Liver Cancer Using Convolutional Neural Network Algorithm

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Authors

Sirajali Nagalpara
Research Scholar, Department of Computer Science, Hemchandracharya North Gujarat University, Patan-384265, Gujarat, India
Bhavesh M. Patel
Assistant Professor, Department of Computer Science, Hemchandracharya North Gujarat University, Patan-384265, Gujarat, India

Abstract


One of the common types of cancer is liver cancer, early detection and diagnosis of which are critical. Discovery, decision, and aggressive therapy can prevent most cancer deaths. We use data mining approaches (Convolutional Neural Networks) to build prediction models for liver cancer with the most widely used statistical analysis methodology. Around 579 records and 10 variables were included in the data collection. The model was built, evaluated, and compared using a k-fold cross-validation process. CNN was the best accurate predictor for this domain with a test set accuracy of 100%.


Keywords


Cancer, Convolutional Neural Networks, data mining, statistical analysis.

References





DOI: https://doi.org/10.17010/ijcs%2F2022%2Fv7%2Fi3%2F171270