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@article{177146,
        author = {Rachakulla Siva rama krishna and P.Saiteja and P.Suredra and Jeeban jyothi},
        title = {Lung cancer detection using CNN model},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {1523-1529},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=177146},
        abstract = {Medical experts categorize lung cancer as the pri- mary deadly cancer which doctors diagnose frequently across the globe leading to many cancer-related deaths. There is an essential need for early lung cancer detection that remains difficult because the disease presents no symptoms until late stages and requires expert interpretation of radiological data. Biopsies together with manual CT scan analysis require extensive times and invasive procedures while also showing susceptibility to human interpretation mistakes. This research investigates Convolutional Neural Networks (CNNs) as a solution to detect lung cancer accurately from computed tomography (CT) scan images.
We developed and executed a deep learning model based on CNN to process CT lung nodule images from LIDC-IDRI dataset for annotation purposes. Strengthening model generalization, the dataset received multiple preprocessing methods that included normalization and both contrast enhancement and data augmen- tation procedures. The CNN consists of consecutive convolutional and pooling operations with dropout layers to combat overfitting results in two-class predictions of cancer areas. Performance evaluations used accuracy and precision recall F1-score to assess the trained model which delivered 95.2 percent accuracy.},
        keywords = {Lung Cancer, Non-Small Cell Lung Cancer, Small Cell Lung Cancer, Deep Learning, Convolutional Neural Net- work (CNN), CT Scan Imaging, Medical Image Analysis, Image Preprocessing, Feature Extraction, Tumor Classifica- tion, Computer-Aided Diagnosis (CAD), Radiology Automa- tion, Artificial Intelligence in Healthcare, Image Segmentation, Cancer Detection System, Binary Classification, Chest CT Scans, Pulmonary Nodule Detection, Grad-CAM Visualiza- tion, Transfer Learning, LIDC-IDRI Dataset, Medical Diag- nostics, Machine Learning, Healthcare Technology},
        month = {May},
        }