Liver Tumor Classification Using Optimal Opposition-Based Grey Wolf Optimization

被引:2
作者
Jose, Reshma [1 ]
Chacko, Shanty [2 ]
Jayakumar, J. [2 ]
Jarin, T. [3 ]
机构
[1] Karunya Inst Technol & Sci, Dept Elect & Commun Engn, Coimbatore, Tamil Nadu, India
[2] Karunya Inst Technol & Sci, Dept Elect & Elect Engn, Coimbatore, Tamil Nadu, India
[3] Jyothi Engn Coll, Dept Elect & Elect Engn, Trichur, Kerala, India
关键词
Image processing; liver cancer; deep belief network; feature extraction; classification; CONVOLUTIONAL NEURAL-NETWORKS; HEPATOCELLULAR-CARCINOMA; CT; DIAGNOSIS;
D O I
10.1142/S0218001422400055
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Image processing plays a significant role in various fields like military, business, healthcare and science. Ultrasound (US), Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) are the various image tests used in the treatment of the cancer. Detecting the liver tumor by these tests is a complex process. Hence, in this research work, a novel approach utilizing a deep learning model is used. That is Deep Belief Network (DBN) with Opposition-Based Learning (OBL)-Grey Wolf Optimization (GWO) is used for the classification of liver cancer. This process undergoes five major processes. Initially, in pre-processing the color contrast is improved by Contrast Limited Adaptive Histogram Equalization (CLAHE) and the noise is removed by Wiener Filtering (WF). The liver is segmented by adaptive thresholding following pre-processing. Following that, the kernelizedFuzzy C Means (FCM) method is used to segment the tumor area. The form, color, and texture features are then extracted during the feature extraction process. Finally, these traits are categorized using DBN, and OBL-GWO is employed to enhance system performance. The entire evaluation is done on Liver Tumor Segmentation (LiTS) benchmark dataset. Finally, the performance of the proposed DBN-OBL-GWO is compared to other models and their achievements are proved. The proposed DBN-OBL-GWO achieves a better accuracy of 0.995, precision of 0.948 and false positive rate (FPR) of 0.116, respectively.
引用
收藏
页数:23
相关论文
共 50 条
  • [41] Fuzzy Based Grey Wolf Optimization for Effective Medical Image Retrieval System
    Yogapriya, J.
    Nithya, B.
    PROCEEDINGS OF THE 2018 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATION AND SIGNAL PROCESSING (ICCSP), 2018, : 1046 - 1050
  • [42] A Feature Selection Method of Parallel Grey Wolf Optimization Algorithm Based on Spark
    Chen, Hongwei
    Han, Lin
    Hu, Zhou
    Hou, Qiao
    Ye, Zhiwei
    Zeng, Jun
    Yuan, Jiansen
    PROCEEDINGS OF THE 2019 10TH IEEE INTERNATIONAL CONFERENCE ON INTELLIGENT DATA ACQUISITION AND ADVANCED COMPUTING SYSTEMS - TECHNOLOGY AND APPLICATIONS (IDAACS), VOL. 1, 2019, : 81 - 85
  • [43] Opposition-based binary competitive optimization algorithm using time-varying V-shape transfer function for feature selection
    Yousef Sharafi
    Mohammad Teshnehlab
    Neural Computing and Applications, 2021, 33 : 17497 - 17533
  • [44] Improved Equilibrium Optimization Algorithm Using Elite Opposition-Based Learning and New Local Search Strategy for Feature Selection in Medical Datasets
    Elgamal, Zenab Mohamed
    Yasin, Norizan Mohd
    Sabri, Aznul Qalid Md
    Sihwail, Rami
    Tubishat, Mohammad
    Jarrah, Hazim
    COMPUTATION, 2021, 9 (06)
  • [45] An effective digit recognition model using enhanced convolutional neural network based chaotic grey wolf optimization
    Preethi, P.
    Asokan, R.
    Thillaiarasu, N.
    Saravanan, T.
    JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2021, 41 (02) : 3727 - 3737
  • [46] Optimal threshold selection for segmentation of Chest X-Ray images using opposition-based swarm-inspired algorithm for diagnosis of pneumonia
    Khosla, Tejna
    Verma, Om Prakash
    MULTIMEDIA TOOLS AND APPLICATIONS, 2024, 83 (09) : 27089 - 27119
  • [47] Grey Wolf optimisation-based feature selection and classification for facial emotion recognition
    Sreedharan, Ninu Preetha Nirmala
    Ganesan, Brammya
    Raveendran, Ramya
    Sarala, Praveena
    Dennis, Binu
    Boothalingam, Rajakumar R.
    IET BIOMETRICS, 2018, 7 (05) : 490 - 499
  • [48] Optimal Text Document Clustering Enabled by Weighed Similarity Oriented Jaya With Grey Wolf Optimization Algorithm
    Venkanna, Gugulothu
    Bharati, K. F.
    COMPUTER JOURNAL, 2021, 64 (06) : 960 - 972
  • [49] Breast cancer diagnosis using support vector machine optimized by improved quantum inspired grey wolf optimization
    Bilal, Anas
    Imran, Azhar
    Baig, Talha Imtiaz
    Liu, Xiaowen
    Nasr, Emad Abouel
    Long, Haixia
    SCIENTIFIC REPORTS, 2024, 14 (01):
  • [50] Diagnosis of diabetes diseases using optimized fuzzy rule set by grey wolf optimization
    Shankar, Siva G.
    Manikandan, K.
    PATTERN RECOGNITION LETTERS, 2019, 125 : 432 - 438