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. 2022 Jul 26:2022:3398156.
doi: 10.1155/2022/3398156. eCollection 2022.

Image Processing and Machine Learning-Based Classification and Detection of Liver Tumor

V Durga Prasad Jasti  1 , Enagandula Prasad  2 , Manish Sawale  3 , Shivlal Mewada  4 , Manoj L Bangare  5 , Pushpa M Bangare  6 , Sunil L Bangare  7 , F Sammy  8
Affiliations

Image Processing and Machine Learning-Based Classification and Detection of Liver Tumor

V Durga Prasad Jasti et al. Biomed Res Int. .

Retraction in

Abstract

The liver is in charge of a plethora of tasks that are critical to healthy health. One of these roles is the conversion of food into protein and bile, which are both needed for digestion. Inhaled and possibly harmful chemicals are flushed from the body. It destroys numerous nutrients acquired through the gastrointestinal system and limits the release of cholesterol by utilizing vitamins, carbohydrates, and minerals stored in the liver. The body's tissues are made up of tiny structures known as cells. Cells proliferate and divide in order to create new ones in the normal sequence of events. When an old or damaged cell has to be replaced, a new cell must be synthesized. In other circumstances, the procedure is a total and utter failure. If the tissues of dead or damaged cells that have been cleared from the body are not removed, they may give birth to nodules and tumors. The liver can produce two types of tumors: benign and malignant. Malignant tumors are more dangerous to one's health than benign tumors. This article presents a technique for the classification and identification of liver cancers that is based on image processing and machine learning. The approach may be found here. During the preprocessing stage of picture creation, the fuzzy histogram equalization method is applied in order to bring about a reduction in image noise. After that, the photographs are divided into many parts in order to zero down on the area of interest. For this particular classification task, the RBF-SVM approach, the ANN method, and the random forest method are all applied.

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Conflict of interest statement

The authors declare that they have no conflict of interest.

Figures

Figure 1
Figure 1
Liver CT scan images.
Figure 2
Figure 2
Methodology for classification and detection of liver tumor.
Figure 3
Figure 3
Accuracy of classifiers for classification of liver tumor images.
Figure 4
Figure 4
Sensitivity of classifiers for classification of liver tumor images.
Figure 5
Figure 5
Specificity of classifiers for classification of liver tumor images.
See this image and copyright information in PMC

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