Detection of Stomach Cancer Using Deep Neural Network in Healthcare Sector

K. Lokesh, Satyajee Srivastava, Ranjana Kumari, S. Arun, S. Padmapriya, R. Krishnamoorthy

20212021 3rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N)12 citationsDOI

Abstract

In this paper, we develop a deep learning model using dense neural network (DenseNet) to detect the gastric cancer in stomach region using computerised tomography (CT) imaging. The image is initially pre-processed and the features are extracted, where these features are used for training the DenseNet to develop a model. The model is tested and validated against various stomach cancer datasets to check the efficacy of the model. The simulation is validated in terms of various performance metrics that include accuracy, precision, recall and f-measure. The results show that the proposed method is effective in improving the rate of detection over various CT images than other methods.

Topics & Concepts

Artificial intelligenceComputer scienceArtificial neural networkDeep learningStomach cancerRecall rateCancerImage (mathematics)Pattern recognition (psychology)Computed tomographyComputer visionRadiologyMedicineInternal medicineRadiomics and Machine Learning in Medical ImagingGastric Cancer Management and OutcomesColorectal Cancer Screening and Detection