Comparative Analysis of Image Processing Methods using GLCM and LBP Feature Extraction for Lung Cancer Detection

Ahnaf, Kern Cesarean; Wahyuni, Elvira Sukma

doi:10.1109/isriti60336.2023.10467244

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Cited by 3 publications

References 13 publications

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NAS FD Lung: A novel lung assist diagnostic system based on neural architecture search

Wang,

2025

Biomedical Signal Processing and Control

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NAS FD Lung: A novel lung assist diagnostic system based on neural architecture search

Wang,

2025

Biomedical Signal Processing and Control

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Feature Extraction Techniques for Patterned Images: A Systematic Literature Review

Wahyuni,

Setyanto,

Utami

et al. 2024

2024 2nd International Conference on Technology Innovation and Its Applications (ICTIIA)

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Explainable lung cancer classification with ensemble transfer learning of VGG16, Resnet50 and InceptionV3 using grad-cam

Kumaran S,

Jeya,

et al. 2024

BMC Med Imaging

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Medical imaging stands as a critical component in diagnosing various diseases, where traditional methods often rely on manual interpretation and conventional machine learning techniques. These approaches, while effective, come with inherent limitations such as subjectivity in interpretation and constraints in handling complex image features. This research paper proposes an integrated deep learning approach utilizing pre-trained models—VGG16, ResNet50, and InceptionV3—combined within a unified framework to improve diagnostic accuracy in medical imaging. The method focuses on lung cancer detection using images resized and converted to a uniform format to optimize performance and ensure consistency across datasets. Our proposed model leverages the strengths of each pre-trained network, achieving a high degree of feature extraction and robustness by freezing the early convolutional layers and fine-tuning the deeper layers. Additionally, techniques like SMOTE and Gaussian Blur are applied to address class imbalance, enhancing model training on underrepresented classes. The model’s performance was validated on the IQ-OTH/NCCD lung cancer dataset, which was collected from the Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases over a period of three months in fall 2019. The proposed model achieved an accuracy of 98.18%, with precision and recall rates notably high across all classes. This improvement highlights the potential of integrated deep learning systems in medical diagnostics, providing a more accurate, reliable, and efficient means of disease detection.

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Comparative Analysis of Image Processing Methods using GLCM and LBP Feature Extraction for Lung Cancer Detection

Cited by 3 publications

References 13 publications

NAS FD Lung: A novel lung assist diagnostic system based on neural architecture search

NAS FD Lung: A novel lung assist diagnostic system based on neural architecture search

Feature Extraction Techniques for Patterned Images: A Systematic Literature Review

Explainable lung cancer classification with ensemble transfer learning of VGG16, Resnet50 and InceptionV3 using grad-cam

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