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@article{184865,
        author = {SYED MEHVEEN and D.MURALI},
        title = {MULTI-STAGE LUNG CANCER CLASSIFICATION USING MACHINE LEARNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {4},
        pages = {3211-3215},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=184865},
        abstract = {Cancer is the uncontrollable cell division of abnormal cells inside the human body, which can spread to other body organs. It is one of the non-communicable diseases (NCDs) and NCDs account for 71% of total deaths worldwide whereas lung cancer is the second most diagnosed cancer after female breast cancer. The cancer survival rate of lung cancer is only 19%. There are various methods for the diagnosis of lung cancer, such as X-ray, CT scan, PET-CT scan, bronchoscopy, and biopsy. However, to know the subtype of lung cancer based on the tissue type H and E staining is widely used, where the staining is done on the tissue aspirated from a biopsy. Studies have reported that the type of histology is associated with the prognosis and treatment of lung cancer. Therefore, early and accurate detection of lung cancer histology is an urgent need and as its treatment is dependent on the type of histology, molecular profile, and stage of the disease, it is essential to analyze the histopathology images of lung cancer. Hence, to speed up the vital process of diagnosis of lung cancer and reduce the burden on pathologists, Deep learning techniques are used. These techniques have shown improved efficacy in the analysis of histopathology slides of cancer. Several studies reported the importance of convolution neural networks (CNN) in the classification of histopathological pictures of various cancer types such as brain, skin, breast, lung, and colorectal cancer. In this study, the tri-category classification of lung cancer images (normal, adenocarcinoma, and squamous cell carcinoma) are carried out by using ResNet 50, VGG-19, Inception_ResNet_V2, and DenseNet121 for the feature extraction and triplet loss to guide the CNN such that it increases inter-cluster distance and reduces intra-cluster distance.},
        keywords = {ResNet 50, CNN, VGG-19, Inception_ResNet_V2 and DenseNet121, Histopathology Images.},
        month = {September},
        }