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@article{175131,
        author = {Sowmya Sree Anumalisetti and Gouri Sankar Nayak and Sri Chaitanya Emandi and Chandrika Koona and Sriraj Kuppili and Manikanta Padyala},
        title = {Detection of Colorectal Cancer},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {1809-1813},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=175131},
        abstract = {A large percentage of cancer- related deaths globally are caused by colorectal cancer, making it a serious global health concern. Improving patient outcomes requires early detection and prompt action. By using deep learning techniques, this initiative offers a novel method for the early diagnosis of colorectal cancer. To create a reliable and accurate deep learning model for the identification of colorectal cancer, we make use of a sizable dataset of medical images, including colonoscopy images. In order to distinguish between cancerous and non-cancerous tissues, Convolutional Neural Networks (CNNs) and the sophisticated ResNet architecture are used to automatically extract significant information from these images.},
        keywords = {Convolutional Neural Networks, NCT-CRC-HE-7k Dataset, ResNet, Colorectal Cancer Detection, Deep Learning (DL), and Medical Image Analysis.},
        month = {April},
        }