Breast Cancer Detection Using Machine Learning & Roboflow Inference

Breast Cancer Detection Using Machine Learning & Roboflow Inference

Vel Priya .V , Mrs.Abirami

Student(IT) – Assistant Professor(IT)

Francis Xavier Engineering College, Tirunelveli, India

ABSTRACT

Breast cancer is one of the leading causes of cancer-related deaths globally, and early detection plays a crucial role in improving patient survival rates. The integration of machine learning and artificial intelligence in medical imaging has shown promising results in enhancing diagnostic accuracy. This paper explores the use of machine learning techniques combined with the RoboFlow platform to develop an efficient breast cancer detection system. RoboFlow provides tools for data preprocessing, augmentation, and model deployment, which simplifies the pipeline for developing high-performance image classification models. The proposed system leverages convolutional neural networks (CNNs) trained on mammogram and ultrasound images to detect the presence of malignant tumors. By employing state-of-the-art deep learning architectures and leveraging RoboFlow's ease of model training and inference, the system aims to provide faster, more accurate, and accessible breast cancer detection. The effectiveness of the model is evaluated using various performance metrics, including accuracy, precision, recall, and F1-score, with the goal of achieving clinical-level reliability in breast cancer screening. The results demonstrate that this approach offers a viable, scalable solution for enhancing early diagnosis and improving patient outcomes