Original scientific paper
https://doi.org/10.1080/00051144.2023.2219099
Novel computer aided diagnostic system using hybrid neural network for early detection of pancreatic cancer
T. Thanya
; Department of ECE, Universal College of Engineering and Technology, Valliyur, India
S. Wilfred Franklin
; Department of ECE, CSI Institute of Technology, Thovalai, India
Abstract
One of the most dangerous tumours in the world, pancreatic cancer (PC), has an unimpressive five-year survival rate of about 5%. An early PC identification is crucial for raising patient survival rates. Diagnosis of PC requires computed tomography (CT), magnetic resonance imaging (MRI) with magnetic resonance cholangiopancreatography (MRCP), or biopsy. The proposed CAD design approach includes image preprocessing, segmentation, feature extraction, and classification phases. Preprocessing is done by using Colour conversion and an isotropic diffusion filter approaches. After that, proposed Fuzzy K-NN Equality algorithm used in segmentation procedures. Deep Learning with feature extraction is used as a classification tool. Tumour cells are classified using the features collected from the pancreatic sample. Train values and testing datasets are part of the image classification criterion. For the purpose of detecting pancreatic cancer, a hybrid Deep Convolutional Neural Network with Deep Belief Network (DCNN_DBN) algorithm is used. According to the experimental findings, the current CAD system offers massive prospects as well as safety in the automated diagnosis of both benign as well as malignant cancers and produces the accuracy of 99.6%. Using this classifier, computing complexity is massively diminished. The suggested technique could be enhanced to detect more pancreatic cancer cell abnormalities.
Keywords
Pancreatic cancer; image classification; computer assisted diagnosis; deep convolutional neural network; deep belief network
Hrčak ID:
315939
URI
Publication date:
7.6.2023.
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