IDRCNN: A Novel Deep Learning Network Model for Pancreatic Ductal Adenocarcinoma Detection on Computed Tomography


  • K. Laxminarayanamma Department of Information Technology, Institute of Aeronautical Engineering, Hyderabad 500043, India
  • R. V. Krishnaiah Electronics and Communication Engineering, Chebrolu Engineering College, Chebrolu (Vi & Md)-522212, Guntur, Andhra Pradesh, India
  • P. Sammulal Computer Science and Engineering, JNTUH, Kondagattu, Karimnagar, Jagtial, Telangana 505501, India.



computed tomography, deep learning, surrounding anatomy, PDAC detection


Early identification of pancreatic ductal adenocarcinoma (PDAC) improves prognosis. Still, it is difficult since lesions are generally smaller and difficult to define on contrast-enhanced computed tomography images (CE-CT). Ineffective PDAC diagnosis has recently been achieved using deep learning models, but the output localized and identified images are of poor quality. This research focuses on small lesions and presents a new, efficient automatic deep-learning network model for PDAC detection. The Improved Deep Residual Convolutional Neural Network (IDRCNN) detects PDAC. The hyperparameter is optimized using the Tunicate Swarm Optimization Algorithm (TSOA) algorithm. A better diagnosis is made due to segmenting the surrounding anatomy structure effects, such as PD. We train a proposed IDRCNN model for segmenting and detecting lesions automatically using CE-CT images. Two more IDRCNN models are trained with the aim of investigating the effects of anatomy integration: (i) segmentation of tumor and pancreas (IDRCNN_TP), and (ii) segmentation of pancreatic Duct (IDRCNN_PD). The three networks' performance was assessed using an external, publicly available test set. Due to its effective classification results, the proposed method produces improved identification results for automated preliminary diagnosis of PDAC in cervical cancer clinics and hospitals. The performance of the proposed method is evaluated using a publicly assessable CT image dataset. It outperforms the existing state-of-the-art methods and achieved 98.67% accuracy, 97.26% recall, 98.52% precision, 97.65% sensitivity, and 98.45% specificity for pancreatic tumor detection.






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