VASC-Net: A Novel AI Framework Combining CNN Architectures with SVM for Lung Cancer Detection
Received: 19 May 2025 | Revised: 18 July 2025, 1 September 2025, and 29 September 2025 | Accepted: 5 October 2025 | Online: 8 December 2025
Corresponding author: S. Lalitha
Abstract
This study introduces a sophisticated image preprocessing method, accompanied by the VASC-Net model (VGG-AlexNet SVM Cancer Network), tailored for lung cancer detection using CT images obtained from the LIDC-IDRI dataset through Kaggle. VASC-Net combines the capabilities of the VGG-19 and AlexNet deep models with Support Vector Machine (SVM) classification, yielding impressive performance results. The model was trained and evaluated on 1000 CT scans from the LIDC-IDRI dataset, with 800 images used for training and 200 for testing. The proposed model achieved 99.79% accuracy, 98.82% specificity, and 99.71% sensitivity. The workflow includes image preprocessing techniques such as resizing, grayscale conversion, contrast enhancement, thresholding, morphological operations, and edge detection. Following that, the Histogram of Oriented Gradients (HoG) method is used for texture-based analysis, and features are then extracted and classified using the VASC-Net model.
Keywords:
lung cancer, deep learning, machine learning, SVMDownloads
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