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@article{174485,
        author = {Chelle Neeraj and Malathi P},
        title = {Enhancing mammogram classification by using CNN with patient assist chatbot},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {4598-4604},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174485},
        abstract = {This paper presents a novel approach to enhancing breast cancer diagnosis through the integration of Convolutional Neural Networks (CNNs) for mammogram classification and a patient-assist chatbot powered by Large Language Models (LLMs). The CNN model classifies mammogram images into three categories—benign, malignant, and normal—with high accuracy, leveraging a large annotated dataset for improved generalization. Complementing the CNN is a chatbot designed using state-of-the-art LLMs, offering advanced capabilities for patient interaction. Unlike traditional Natural Language Processing (NLP) systems, LLMs provide dynamic, context-aware, and multilingual responses, enabling patients to understand diagnostic results, clarify medical terminologies, and receive actionable recommendations. Moreover, the chatbot addresses patient anxiety through empathetic interactions and emotional support.
By combining diagnostic precision with personalized patient care, this system bridges the gap between advanced medical imaging technologies and patient-centric healthcare. The proposed solution aims to alleviate the workload of radiologists, improve diagnostic workflows, and ensure that patients receive comprehensive and compassionate support. This integrated approach holds potential for widespread clinical adoption, providing a significant step forward in both breast cancer detection and patient engagement.},
        keywords = {Mammogram Classification, Convolutional Neural Networks (CNN), Large Language Models (LLM), Breast Cancer Detection, Artificial Intelligence in Healthcare, Medical Image Analysis, Patient Engagement, Early Cancer Diagnosis.},
        month = {March},
        }