Bimonthly    Since 1986
ISSN 1004-9037
Publication Details
Edited by: Editorial Board of Journal of Data Acquisition and Processing
P.O. Box 2704, Beijing 100190, P.R. China
Sponsored by: Institute of Computing Technology, CAS & China Computer Federation
Undertaken by: Institute of Computing Technology, CAS
Published by: SCIENCE PRESS, BEIJING, CHINA
Distributed by:
China: All Local Post Offices
 
   
      1 Jan 2024, Volume 39 Issue 1   
    Article

    INTEGRATING SURF FEATURE REDUCTION WITH CCAPSNETS CLASSIFICATION FOR IMPROVING THE ACCURACY OF LUNG CANCER DETECTION
    Suman Antony Lasrado, Dr. G N K Suresh Babu
    Journal of Data Acquisition and Processing, 2024, 39 (1): 1045-1063 . 

    Abstract

    Lung cancer continues one of the indicating causes of cancer-linked transience worldwide, compelling the advancement of accurate and efficient investigative tools. In this research, we propose a novel approach for lung cancer detection utilizing feature reduction with Speeded-Up Robust Features (SURF) and classification with Classification Capsule Networks (CCapsNets). The researcher conduct experiments on the UC Irvine Machine Learning Repository lung cancer dataset, which comprises a diverse collection of computed tomography (CT) images. Firstly, SURF is used to isolate robust and discriminative attributes from the lung CT images. SURF's ability to detect local features invariant to scale and rotation enables effective representation of the lung tissue characteristics. Next, CCapsNets us utilized, a state-of-the-art deep learning architecture known for its ability to capture hierarchical relationships within data, for lung cancer classification. CCapsNets leverage capsule networks to preserve spatial hierarchies and improve generalization performance, particularly in medical image analysis tasks. The investigational results exhibit the efficacy of the intended methodology in lung cancer detection. By integrating SURF feature reduction with CCapsNets classification, superior accuracy og 98.6% is achieved in evaluation to traditional methods. Furthermore, the interpretability of CCapsNets enables insights into the learned features and contributes to the understanding of lung cancer imaging biomarkers. This research work presents a promising framework for lung cancer detection, leveraging advanced image processing techniques and deep learning methodologies. The proposed approach holds significant potential for enhancing early diagnosis and prognosis prediction in clinical settings, thereby improving patient role conclusions and reducing the liability of lung cancer morbidity and mortality.

    Keyword

    Lung cancer detection, SURF, CCapsNets, UC Irvine Machine Learning Repository, Computed tomography


    PDF Download (click here)

SCImago Journal & Country Rank

ISSN 1004-9037

         

Home
Editorial Board
Author Guidelines
Subscription
Journal of Data Acquisition and Processing
Institute of Computing Technology, Chinese Academy of Sciences
P.O. Box 2704, Beijing 100190 P.R. China
E-mail: info@sjcjycl.cn
 
  Copyright ©2015 JCST, All Rights Reserved