Evidence map›Paper›PMID 39773067›Full record

ArticleCurrent medical imaging2025

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

Mohd Munazzer Ansari, Shailendra Kumar, Md Belal Bin Heyat, Hadaate Ullah, Mohd Ammar Bin Hayat, Sumbul, Saba Parveen, Ahmad Ali, Tao Zhang

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Article in Current medical imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
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5citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Mohd Munazzer AnsariDepartment of Electronic and Communication Engineering, Integral University, Lucknow, India.
Shailendra KumarDepartment of Electronic and Communication Engineering, Integral University, Lucknow, India.
Md Belal Bin HeyatCenBRAIN Neurotech Center of Excellence, School of Engineering, Westlake University, Hangzhou, Zhejiang, China.
Hadaate UllahDepartment of Electrical and Electronic Engineering, University of Science and Technology Chittagong, Bangladesh.
Mohd Ammar Bin HayatCollege of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, China.
SumbulDepartment of Ilmul Qabalat wa Amraze Niswan, University College of Unani, Tonk, Rajasthan, India.
Saba ParveenCollege of Electronics and Information Engineering, Shenzhen University, Shenzhen, China.
Ahmad AliCollege of Computer Science and Software Engineering Shenzhen University, Shenzhen, China.
Tao ZhangSchool of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan611731, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND AND

objectiveLung 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).

methodsUsing the LIDC-IDRI dataset, we pre-processed images with a median filter and histogram equalization, segmented lung tumors through thresholding and edge detection, and extracted geometric features such as area, perimeter, eccentricity, compactness, and circularity. VGGNet-16 and SVM employed for feature extraction and classification, respectively. Performance matrices were evaluated using accuracy, AUC, recall, precision, and F1-score. Both VGGNet-16 and SVM underwent comparative analysis during the training, validation, and testing phases.

resultsThe SVMVGGNet-16 model outperformed both, with a training accuracy (97.22%), AUC (99.42%), recall (94.22%), precision (95.28%), and F1- score (94.68%). In testing, our SVMVGGNet-16 model maintained high accuracy (96.72%), with an AUC (96.87%), recall (84.67%), precision (87.40%), and F1-score (85.73%).

conclusionOur experimental results demonstrate the potential of SVMVGGNet-16 in improving diagnostic performance, leading to earlier detection and better treatment outcomes. Future work includes refining the model, expanding datasets, conducting clinical trials, and integrating the system into clinical practice to ensure practical usability.

Indexed as

Deep LearningLung NeoplasmsSupport Vector MachineTomography, X-Ray ComputedConvolutional Neural NetworksHumansNeural Networks, ComputerReproducibility of ResultsAIBio-imagingCancerDeep learningDiagnosisDiseaseHealthcareImage classificationMedical intelligence.Medical machine learning

Identifiers

PMID39773067
PMCPMC12813548

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