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phat 24(1):

Research Article

Performance Assessment of Deep Learning Models on Non-Small Cell Lung Cancer Type Classification

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  • @ARTICLE{10.4108/eetpht.10.6423,
        author={K. Ezhilraja and P. Shanmugavadivu},
        title={Performance Assessment of Deep Learning Models on Non-Small Cell Lung Cancer Type Classification},
        journal={EAI Endorsed Transactions on Pervasive Health and Technology},
        volume={10},
        number={1},
        publisher={EAI},
        journal_a={PHAT},
        year={2024},
        month={12},
        keywords={Lung Cancer Classification, Lung Image Enhancement, Contrast Enhancement, Lung Feature Extraction, Lung Cancer Detection},
        doi={10.4108/eetpht.10.6423}
    }
    
  • K. Ezhilraja
    P. Shanmugavadivu
    Year: 2024
    Performance Assessment of Deep Learning Models on Non-Small Cell Lung Cancer Type Classification
    PHAT
    EAI
    DOI: 10.4108/eetpht.10.6423
K. Ezhilraja1,*, P. Shanmugavadivu1
  • 1: Gandhigram Rural Institute
*Contact email: kezhilraja3@gmail.com

Abstract

In recent years, lung cancer incidents are very high with equally high mortality rate. The main reason for fatal incidences is the late diagnosis and confirmation of the disease at an advanced stage. Identification of the disease at an early stage using lung Computed Tomography (CT) offers tremendous scope for timely medical intervention. The article illustrates the use of deep transfer learning-based pre-trained models for the diagnosis of Non-Small Cell Lung Cancer (NSCLC). The datasets were chosen from Chest CT Scan Images and the Lung Image Database Consortium (LIDC), containing over 3,179 images depicting three NSCLC types, namely, normal, adenocarcinoma, squamous cell carcinoma, and large cell carcinoma. The process is designed to measure the accuracy of NSCLC detection with an experimental dataset using approaches with and without pre-processing of lung images. The transfer learning models use deep learning and produce good results in prediction and classification. The image dataset was first handled by the convolutional neural networks DenseNet121, ResNet50, InceptionV3, VGG16, Xception, and VGG19. As a second phase, input images were subjected to contrast/brightness enhancement using Multi Level Dualistic Sub Image Histogram Equalization (ML-DSIHE). Enhanced images were further processed using shape-based feature extraction. Finally, those features input to CNN models and the results recorded. Among these models, VGG16 achieved the highest accuracy of 81.42% using the original dataset and 91.64% with the enhanced dataset. The performance of these two approaches was also evaluated using Precision, Recall, F1-Score, Accuracy, and Loss. It is confirmed that VGG16 gives highly reliable accuracy when trained upon enhanced images.

Keywords
Lung Cancer Classification, Lung Image Enhancement, Contrast Enhancement, Lung Feature Extraction, Lung Cancer Detection
Received
2024-12-04
Accepted
2024-12-04
Published
2024-12-04
Publisher
EAI
http://dx.doi.org/10.4108/eetpht.10.6423

Copyright © 2024 Ezhilraja, K. et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.

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