SVMVGGNet-16: A Novel Machine and Deep Learning Based Approaches for Lung Cancer Detection Using Combined SVM and VGGNet-16.

Journal: Current medical imaging
Published Date: Jan 1, 2025

Abstract

BACKGROUND AND OBJECTIVE: Lung cancer remains a leading cause of cancer-related mortality worldwide, necessitating early and accurate detection methods. Our study aims to enhance lung cancer detection by integrating VGGNet-16 form of Convolutional Neural Networks (CNNs) and Support Vector Machines (SVM) into a hybrid model (SVMVGGNet-16), leveraging the strengths of both models for high accuracy and reliability in classifying lung cancer types in different 4 classes such as adenocarcinoma (ADC), large cell carcinoma (LCC), Normal, and squamous cell carcinoma (SCC).

Authors

  • Mohd Munazzer Ansari
    Department of Electronic and Communication Engineering, Integral University, Lucknow, India.
  • Shailendra Kumar
    Department of Electronics and Communication Engineering, Integral University Lucknow, Uttar Pradesh, India.
  • Md Belal Bin Heyat
    CenBRAIN Neurotech Center of Excellence, School of Engineering, Westlake University, Hangzhou, Zhejiang, China.
  • Hadaate Ullah
    Department of Electrical and Electronic Engineering, University of Science and Technology Chittagong, Bangladesh.
  • Mohd Ammar Bin Hayat
    College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, China.
  • Sumbul
    Department of Ilmul Qabalat wa Amraze Niswan, University College of Unani, Tonk, Rajasthan, India.
  • Saba Parveen
    College of Electronics and Information Engineering, Shenzhen University, Shenzhen, China.
  • Ahmad Ali
    Center for Plant Sciences and Biodiversity, University of Swat, Pakistan.
  • Tao Zhang
    Department of Traumatology, Chongqing Emergency Medical Center, Chongqing University Central Hospital, School of Medicine, Chongqing University, Chongqing, 40044, People's Republic of China.

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