Abstract
Objective: The growth of anomalous cells in the human body in an uncontrolled manner is characterized as cancer. The detection of cancer is a multi-stage process in the clinical examination. Methods: It is mainly involved with the assistance of radiological imaging. The imaging technique is used to identify the spread of cancer in the human body. This imaging-based detection can be improved by incorporating the Image Processing methodologies. In image processing, the preprocessing is applied at the lower-level abstraction. It removes the unwanted noise pixel present in the image, which also distributes the pixel values based on the specific distribution method. Results: Neural Network is a learning and processing engine, which is mainly used to create cognitive intelligence in various domains. In this work, the Neural Network (NN) based filtering approach is developed to improve the preprocessing operation in the cancer detection process. Conclusion: The performance of the proposed filtering method is compared with the existing linear and non-linear filters in terms of Mean Squared Error (MSE), Peak Signal to Noise Ratio (PSNR) and Image Enhancement Factor (IEF).
Highlights
MethodsIt is mainly involved with the assistance of radiological imaging
HE abnormal cell growth and multiplying n exponential distribution is marked as cancer
The validation of linear time-invariant operation is applied in the Wiener filter to eliminate the noise pixel region in the image. It identifies the lower and upper bound for the filtering instead of Neural Network Based Filtering Method
Summary
It is mainly involved with the assistance of radiological imaging. The imaging technique is used to identify the spread of cancer in the human body. This imaging-based detection can be improved by incorporating the Image Processing methodologies. The preprocessing is applied at the lower-level abstraction. It removes the unwanted noise pixel present in the image, which distributes the pixel values based on the specific distribution method
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