Influence of grey wolf optimization feature selection on gradient boosting machine learning techniques for accurate detection of liver tumor
Abstract Malignant growth in liver results in liver tumor. The most common types of liver cancer are primary liver disease and secondary liver disease. Most malignant growths are benign tumors, and the condition they cause, essential liver disease, is the end result. Cancer of the liver is a potenti...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2023-06-01
|
| Series: | SN Applied Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s42452-023-05405-9 |
| _version_ | 1797806592087818240 |
|---|---|
| author | Malik Jawarneh José Luis Arias-Gonzáles Dattatray P. Gandhmal Rami Qays Malik Kantilal Pitambar Rane Batyrkhan Omarov Cosmena Mahapatra Mohammad Shabaz |
| author_facet | Malik Jawarneh José Luis Arias-Gonzáles Dattatray P. Gandhmal Rami Qays Malik Kantilal Pitambar Rane Batyrkhan Omarov Cosmena Mahapatra Mohammad Shabaz |
| author_sort | Malik Jawarneh |
| collection | DOAJ |
| description | Abstract Malignant growth in liver results in liver tumor. The most common types of liver cancer are primary liver disease and secondary liver disease. Most malignant growths are benign tumors, and the condition they cause, essential liver disease, is the end result. Cancer of the liver is a potentially fatal disease that can only be cured by combining a number of different treatments. Machine learning, feature selection and image processing have the capability to provide a framework for the accurate detection of liver diseases. The processing of images is one of the components that come together to form this group. When utilized for the purpose of reviewing previously recorded visual information, the instrument performs at its highest level of effectiveness. The importance of feature selection on machine learning algorithms for the early and accurate diagnosis of liver tumors is discussed in this article. The input consists of images from a CT scan of the liver. These images are preprocessed by discrete wavelet transform. Discrete wavelet transforms increase resolution by compressing the images. Images are segmented in parts to identify region of interest by K Means algorithm. Features are selected by grey wolf optimization technique. Classification is performed by Gradient boosting, support vector machine and random forest. GWO Gradient boosting is performing better in accurate classification and prediction of liver cancer. |
| first_indexed | 2024-03-13T06:09:36Z |
| format | Article |
| id | doaj.art-9f27697682ec42ac96a26481cef20de1 |
| institution | Directory Open Access Journal |
| issn | 2523-3963 2523-3971 |
| language | English |
| last_indexed | 2024-03-13T06:09:36Z |
| publishDate | 2023-06-01 |
| publisher | Springer |
| record_format | Article |
| series | SN Applied Sciences |
| spelling | doaj.art-9f27697682ec42ac96a26481cef20de12023-06-11T11:22:19ZengSpringerSN Applied Sciences2523-39632523-39712023-06-01571910.1007/s42452-023-05405-9Influence of grey wolf optimization feature selection on gradient boosting machine learning techniques for accurate detection of liver tumorMalik Jawarneh0José Luis Arias-Gonzáles1Dattatray P. Gandhmal2Rami Qays Malik3Kantilal Pitambar Rane4Batyrkhan Omarov5Cosmena Mahapatra6Mohammad Shabaz7Faculty of Computing Sciences, Gulf CollegeUniversity of British ColumbiaDepartment of Computer Science and Engineering, N. B, N. B. Navale Sinhgad College of EngineeringMedical Instrumentation Techniques Engineering Department, College of Engineering and Technologies, Al-Mustaqbal UniversityDepartment of Electronics and Telecommunications Engineering, Bharati Vidyapeeth College of EngineeringAl-Farabi Kazakh National UniversityVivekananda Institute of Professional StudiesArba Minch UniversityAbstract Malignant growth in liver results in liver tumor. The most common types of liver cancer are primary liver disease and secondary liver disease. Most malignant growths are benign tumors, and the condition they cause, essential liver disease, is the end result. Cancer of the liver is a potentially fatal disease that can only be cured by combining a number of different treatments. Machine learning, feature selection and image processing have the capability to provide a framework for the accurate detection of liver diseases. The processing of images is one of the components that come together to form this group. When utilized for the purpose of reviewing previously recorded visual information, the instrument performs at its highest level of effectiveness. The importance of feature selection on machine learning algorithms for the early and accurate diagnosis of liver tumors is discussed in this article. The input consists of images from a CT scan of the liver. These images are preprocessed by discrete wavelet transform. Discrete wavelet transforms increase resolution by compressing the images. Images are segmented in parts to identify region of interest by K Means algorithm. Features are selected by grey wolf optimization technique. Classification is performed by Gradient boosting, support vector machine and random forest. GWO Gradient boosting is performing better in accurate classification and prediction of liver cancer.https://doi.org/10.1007/s42452-023-05405-9Grey wolf optimizationGradient boostingDiscrete wavelet transformK means algorithm |
| spellingShingle | Malik Jawarneh José Luis Arias-Gonzáles Dattatray P. Gandhmal Rami Qays Malik Kantilal Pitambar Rane Batyrkhan Omarov Cosmena Mahapatra Mohammad Shabaz Influence of grey wolf optimization feature selection on gradient boosting machine learning techniques for accurate detection of liver tumor SN Applied Sciences Grey wolf optimization Gradient boosting Discrete wavelet transform K means algorithm |
| title | Influence of grey wolf optimization feature selection on gradient boosting machine learning techniques for accurate detection of liver tumor |
| title_full | Influence of grey wolf optimization feature selection on gradient boosting machine learning techniques for accurate detection of liver tumor |
| title_fullStr | Influence of grey wolf optimization feature selection on gradient boosting machine learning techniques for accurate detection of liver tumor |
| title_full_unstemmed | Influence of grey wolf optimization feature selection on gradient boosting machine learning techniques for accurate detection of liver tumor |
| title_short | Influence of grey wolf optimization feature selection on gradient boosting machine learning techniques for accurate detection of liver tumor |
| title_sort | influence of grey wolf optimization feature selection on gradient boosting machine learning techniques for accurate detection of liver tumor |
| topic | Grey wolf optimization Gradient boosting Discrete wavelet transform K means algorithm |
| url | https://doi.org/10.1007/s42452-023-05405-9 |
| work_keys_str_mv | AT malikjawarneh influenceofgreywolfoptimizationfeatureselectionongradientboostingmachinelearningtechniquesforaccuratedetectionoflivertumor AT joseluisariasgonzales influenceofgreywolfoptimizationfeatureselectionongradientboostingmachinelearningtechniquesforaccuratedetectionoflivertumor AT dattatraypgandhmal influenceofgreywolfoptimizationfeatureselectionongradientboostingmachinelearningtechniquesforaccuratedetectionoflivertumor AT ramiqaysmalik influenceofgreywolfoptimizationfeatureselectionongradientboostingmachinelearningtechniquesforaccuratedetectionoflivertumor AT kantilalpitambarrane influenceofgreywolfoptimizationfeatureselectionongradientboostingmachinelearningtechniquesforaccuratedetectionoflivertumor AT batyrkhanomarov influenceofgreywolfoptimizationfeatureselectionongradientboostingmachinelearningtechniquesforaccuratedetectionoflivertumor AT cosmenamahapatra influenceofgreywolfoptimizationfeatureselectionongradientboostingmachinelearningtechniquesforaccuratedetectionoflivertumor AT mohammadshabaz influenceofgreywolfoptimizationfeatureselectionongradientboostingmachinelearningtechniquesforaccuratedetectionoflivertumor |