Abstract
The liver is present underneath the diaphragm and extends from right to left upper part of the belly. The liver is an organ which has many responsibilities for producing different chemicals needed for physical body. The conversion of an image into information is helpful for research people to share. The method of conversion prevents manual error because it depends on technology and algorithm. This paper deals with the detection of liver cancer as implemented using optimization techniques. The paper discusses scanning, filtering, segmentation, feature extraction, and exhibit through artificial neural network. In real time data sets, feed forward neural network is applied for classification and detection of liver cancer. Filtering is mainly used to reduce noise and smoothing edges. Then segmentation is used to extract needed region so that result can be stored in less storage space. Feature extraction is done through gray level and co-occurrence matrix that can yield the result in various parameters. This matrix is helpful to differentiate the tumors namely benign and malignant. Artificial neural network is trained to differentiate those tumors. The result is analyzed by following the parameters like accuracy, area, correlation, entropy, homogeneity, contrast, and similarity index.
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20 June 2022
This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1007/s12652-022-04194-0
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This article has been retracted. Please see the retraction notice for more detail:https://doi.org/10.1007/s12652-022-04194-0
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Hemalatha, V., Sundar, C. RETRACTED ARTICLE: Automatic liver cancer detection in abdominal liver images using soft optimization techniques. J Ambient Intell Human Comput 12, 4765–4774 (2021). https://doi.org/10.1007/s12652-020-01885-4
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DOI: https://doi.org/10.1007/s12652-020-01885-4