Automated lung cancer detection using artificial intelligence techniques
Lung cancer is a leading cause of cancer-related mortality due to delayed diagnosis and limitations of conventional screening methods. This study presents an artificial neural network-based computer-aided diagnosis system for automated lung cancer detection using computed tomography scans. The proposed methodology integrates advanced digital image processing techniques with machine learning-based classification to improve diagnostic accuracy and reliability. The framework consists of multiple stages. Initially, computed tomography images are preprocessed using a two-dimensional median filter to suppress noise while preserving structural boundaries. Morphological operations and contrast enhancement techniques are applied, followed by adaptive thresholding to segment lung regions. A seeded region growing technique is then employed to identify suspicious regions and extract relevant image segments. From these segments, 25 texture features are computed using the gray level co-occurrence matrix, capturing statistical properties such as contrast, correlation, entropy, homogeneity, and energy. Two artificial neural network classifiers, namely the back propagation neural network and the radial basis function neural network, are trained using these features. A dataset of 500 computed tomography images, including both cancerous and non-cancerous cases, is used for performance evaluation. Experimental results demonstrate that both models achieve high classification accuracy, sensitivity, and specificity, while the radial basis function neural network consistently outperforms the back propagation neural network, achieving a maximum accuracy of 94%. These findings highlight the effectiveness of the proposed system as a reliable, non-invasive, and computationally efficient tool for early lung cancer detection, with potential applicability to other medical imaging domains.
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