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@article{206755,
        author = {Harshini K and Jithendra P R Nayak},
        title = {Automated Lung Cancer Detection and Classification System Using Machine Learning and Advanced Image Processing Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {no},
        pages = {335-343},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=206755},
        abstract = {Detecting lung cancer at an early stage is important for improving survival chances and enabling timely treatment. However, examining medical images manually can be slow and may lead to errors, especially when the data is large and the differences between images are subtle. This study introduces a lung cancer detection and classification systems that uses machine learning along with the image processing methods to address these challenges. This technique utilizes a series of methodologies for extracting relevant data from the lungs' images, such as the use of histograms, HOG, color-based, and edge detection methods. This will aid in the extraction of the structural features in addition to the textures contained within the image. This data is used by the Random Forest Classifier in determining the classes of the images, which include adenocarcinoma, squamous cell carcinoma, and normal classes. The system is made available via an interactive web-based interface developed using the Flask framework, which ensures that users can easily input images and get fast results. The use of various feature extraction techniques increases the efficiency and robustness of the algorithm relative to utilizing just one approach. From the analysis above, it can be seen that the model is able to provide accurate classification results and thus can be effectively used by medical practitioners during their diagnosis. In conclusion, the proposed method offers a scalable way of automatically detecting lung cancer, which will play a vital role in facilitating its early diagnosis.},
        keywords = {.},
        month = {July},
        }