Pancreatic Cancer Detection using Neural Network


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 using the ultrasound images for processing. The objective of this study was to evaluate the feasibility of pancreatic tumor detection by high-intensity focused ultrasound image. For detecting pancreatic cancer mostly machine learning techniques are used. In this paper we proposed propoblastic neural network method for diagnosed pancreatic cancer. The aim of this work is to compare and explain how NN and logistic algorithm provide better solution when its 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 logistic algorithm is achieved 93.50% accuracy from another machine learning algorithm.


As the main steps of the proposed were types of cancer identification, it consists of image processing: preprocessing, transformation, feature extraction, and cancer classification. By applying these 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


Digital image processing deals with?manipulation of digital images through a digital computer. It is a subfield of signals and systems but focus particularly on images. DIP focuses on developing a computer system that is able to perform?processing?on an?image. The input of that system is a digital?image?and the system process that?image?using efficient algorithm

?It allows a much wider range of algorithms to be applied to the input data and can avoid problems such as the build-up of noise and distortion during processing.

  1. Importing the image via image acquisition tools;
  2. Analyzing and manipulating the image;
  3. Output in which result can be altered image

Image Pre-processing?is a common name for operations with?images?at the lowest level of abstraction. Its input and output are intensity?images. The aim of?pre-processing?is an improvement of the?image?data that suppresses unwanted distortions or enhances some image?features important for further processing.

Discrete Wavelet Transform (DWT)

The discrete wavelet remodel (DWT) became superior to use the wavelet rework to the digital international. Filter banks are used to approximate the behavior of the non-prevent wavelet remodel. The sign is decomposed with a immoderate-skip smooth out and a low-bypass clear out. The coefficients of these filters are computed using mathematical evaluation and made to be had to you. See Appendix B for more records about those computations.

2.2 Discrete Wavelet Transform


LP d: Low Pass Decomposition Filter

HP d: High Pass Decomposition Filter

LP r: Low Pass Reconstruction Filter

HP r: High Pass Reconstruction Filter

The wavelet literature offers the filter coefficients to you in tables. An example is the Daubechies filters for wavelets. These filters rely upon a parameter p called the vanishing 2nd.

Gray-Level Co-Occurrence Matrix:

To create a GLCM, use the?graycomatrix?function. The?graycomatrix?function creates a gray-level co-occurrence matrix (GLCM) by calculating how often a pixel with the intensity (gray-level) value?i?occurs in a specific spatial relationship to a pixel with the value?j. By default, the spatial relationship is defined as the pixel of interest and the pixel to its immediate right (horizontally adjacent), but you can specify other spatial relationships between the two pixels. Each element (i,j) in the resultant?GLCM is simply the sum of the number of times that the pixel with value?i?occurred in the specified spatial relationship to a pixel with value?j?in the input image. Because the processing required to calculate a GLCM for the full dynamic range of an image is prohibitive,?graycomatrix?scales the input image. By default,?graycomatrix?uses scaling to reduce the number of intensity values in gray scale image from 256 to eight. The number of gray levels determines the size of the GLCM. To control the number of gray levels in the GLCM and the scaling of intensity values, using the?Num Levels?and the?Gray Limits parameters of the?graycomatrix?function.

Neural Networks (NN):

Neural Network (NN) and General Regression Neural Networks (GRNN) have similar architectures, but there is a fundamental difference: networks perform classification where the target variable is categorical, whereas general regression neural networks perform regression where the target variable is continuous. If you select a NN/GRNN network, DTREG will automatically select the correct type of network based on the type of target variable.

Hardware Requirements

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



[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 themanagement of patients with increased risk for familial pancreatic cancer. Gut, 62(3):339?47, 2013. [4] F. Chollet et al. Keras. 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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