AI-DRIVEN DETECTION OF LUNG CARCINOMA: LEVERAGING DEEP LEARNING IN CHEST CT ANALYSIS
DOI:
https://doi.org/10.63503/c.acset.2025.14Keywords:
Deep Learning, CNN, CT scans, Lung Cancer, AIAbstract
Lung diseases encompass a range of conditions that impair lung function and disrupt the respiratory system. Among these, lung carcinoma remains one of the leading causes of mortality worldwide. Early detection significantly improves survival prospects, as diagnosing lung cancer at an initial stage is crucial for effective treatment. Over time, early identification has contributed to an increase in the average survival rate for lung cancer patients, rising from 14 percent to 49 percent. Computed tomography (CT) imaging plays a critical role in this diagnostic process, offering higher sensitivity and accuracy compared to traditional X-ray imaging. Several imaging methods that complement one another. A deep neural network for lung cancer detection. The ability to detect cancer using CT pictures has been created and tested. In order to classify the lung picture as whether it's benign or malignant, a densely linked convolutional neural network (DenseNet) and adaptive learning can help. The researchers utilized a dataset of 201 lung scans, with 90 percent of the pictures being positive. 10 percent of the data are utilized for testing and classification, while the rest are used for training. The proposed approach obtained an accuracy of 97.50% in tests, according to the results.
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