Cancer Cell Detection and Segmentation System Using Deep Learning

Abstract - The primary objective of the cancer cell detection and segmentation system is to develop an automated solution that can accurately identify and localize cancer cells within microscopic images. This system utilizes advanced deep learning techniques, particularly convolutional neural networks, to analyze the intricate patterns and subtle features within medical images, enabling the distinction between healthy and cancerous cells. Early detection of cancer cells is crucial for providing proper treatment to patients and reducing the risk of mortality, as detection at later stages leads to increased suffering and higher mortality rates. Cancer cells exhibit significant variations in shape and size, with darker and larger nuclei compared to normal cells, making their detection a challenging task. Our system leverages state-of-the-art deep learning models for image processing and segmentation, achieving high accuracy in cancer cell detection. The developed solution includes a comprehensive web-based interface with user authentication, a chatbot for assistance, history tracking, and personalized treatment recommendations including precautions and diet plans. The system has been deployed on the cloud to make it accessible for real-time use by healthcare professionals. Experimental results demonstrate the system's effectiveness in detecting and segmenting cancer cells with high precision, potentially improving early diagnosis and patient outcomes. Key Words: Cancer Cell Detection, Segmentation, Deep Learning, Convolutional Neural Networks, Medical Imaging, Early Detection,Cloud Deployment.