An Investigating the Detection of Lung Cancer by Utilising Deep Learning Algorithms
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Lung cancer remains one of the leading causes of cancer‑related death worldwide, underscoring the urgent need for accurate, early‑stage diagnostic methods. Our approach begins by applying Analysis of Variance (ANOVA) to identify the most discriminative imaging features between malignant and benign regions. We then employ Principal Component Analysis (PCA) to reduce feature dimensionality, thereby lowering computational complexity and improving model generalization. The reduced feature set is used to train a Support Vector Machine (SVM) classifier, which distinguishes cancerous tissue from healthy lung parenchyma. In parallel, we fine‑tune a ResNet‑50 convolutional neural network to perform both regression and classification tasks directly on the raw CT image patches. Evaluation on publicly available benchmark datasets demonstrates that our combined ANOVA–PCA–SVM pipeline and ResNet‑50 model achieve superior performance metrics—exhibiting high accuracy, sensitivity, and specificity—when compared to contemporary methods. These results validate the efficacy of our hybrid framework for rapid and reliable lung cancer screening.
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