Pancreatic Cancer Detection using Neural Network

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Description

Pancreatic cancer is associated with poor clinical outcomes primarily due to the advanced stage at the time of diagnosis. Endoscopic harmonic ultrasound imaging (HI) is being used to characterize pancreatic masses. In this proposed method we use ultrasound images for processing. The objective of this study was to evaluate the feasibility of pancreatic tumor detection by high-intensity focused ultrasound images. For detecting pancreatic cancer mostly machine learning techniques are used. In this paper, we proposed a trophoblastic neural network method for diagnosing pancreatic cancer. The aim of this work is to compare and explain how NN and logistic algorithms provide better solutions when they work with ensemble machine learning algorithms for diagnosing Pancreatic cancer even the variables are reduced. In this paper, we used the Pancreatic Cancer dataset. When compared to related work from the literature, it is shown that the PNN approach with a logistic algorithm achieved 93.50% accuracy from another machine learning algorithm.


Introduction:

The main steps of the proposed types of cancer identification consist of image processing: preprocessing, transformation, feature extraction, and cancer classification. By applying this series of image processing techniques, we can automate the procedure of cancer identifications. Its most serious limitation is reliance on the performance of a human operator for diagnostic accuracy. This is pursued by developing a digital image processing system to automate the examination of pancreatic cancer. The system must differentiate types of pancreatic cancer and the next section briefly introduces the overview of the proposed cancer diagnosis system based on image processing techniques.


Block Diagram

Pancreatic Cancer Detection using Neural Network


Hardware Requirements

  • system
  • 4 GB of RAM
  • 500 GB of Hard disk

SOFTWARE REQUIREMENTS:


REFERENCES:

[1] Y. Bengio, P. Simard, and P. Frasconi. Learning long-term dependencies with gradient descent is difficult. IEEE transactions on neural networks, 5(2):157? 166, 1994.

[2] J. Cai, L. Lu, Y. Xie, F. Xing, and L. Yang. Pancreas segmentation in MRI using graph-based decision fusion on convolutional neural networks. In MICCAI 2017, pages 674? 682, 2017.

[3] M. I. Canto, F. Harinck, R. H. Hruban, G. J. Offerhaus, J. W. Poley, I. Kamel, Y. Nio, R. S. Schulick, C. Bassi, I. Kluijt, M. J. Levy, A. Chak, P. Fockens, M. Goggins, and M. Bruno. International Cancer of the pancreas screening (caps) consortium summit on the management of patients with increased risk for familial pancreatic cancer. Gut, 62(3):339? 47, 2013. [4] F. Chollet et al. Keras. https://github.com/fchollet/ keras, 2015.

[5] K. Dmitriev, A. E. Kaufman, A. A. Javed, R. H. Hruban, E. K. Fishman, A. M. Lennon, and J. H. Saltz. Classification of pancreatic cysts in computed tomography images using a random forest and convolutional neural network ensemble. In MICCAI 2017, pages 150? 158, 2017.


 

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