Download PDFOpen PDF in browserComputer Vision in Healthcare for Breast Cancer Detection11 pages•Published: August 6, 2024AbstractComputer vision is a rapidly advancing field with profound implications for the healthcare industry. This technology leverages the power of artificial intelligence and image processing to extract valuable insights from medical images and videos. So in my project breast cancer is taken as priority. In contemporary healthcare, the timely and accurate detection of breast cancer is paramount to improving patient outcomes and reducing mortality rates. Breast Ultrasound Imaging (BUSI) stands as a pivotal non- invasive tool in this endeavor. However, to unlock its full potential, advanced image processing techniques are imperative. OpenCV, a versatile computer vision library, plays a critical role in this context and specifically tailored for analyzing BUSI ultrasound images. The methodology encompasses key stages. A meticulously annotated datasets of BUSI ultrasound images, discerning the presence or absence of breast cancer, is curated. Leveraging OpenCV capabilities, the datasets undergoes pre- processing, including re-sizing, normalization, and enhancement, to optimize an image quality for subsequent analysis. MobileNetV2, selected for its computational efficiency, serves as the foundation for transfer learning, synergistically integrated with OpenCV for robust feature extraction. The trained MobileNetV2 model, enriched by OpenCV image processing capabilities, undergoes rigorous evaluation on an independent test set, employing various performance metrics. This assessment aims to quantify the model's proficiency in breast cancer detection. The amalgamation of OpenCV and MobileNetV2 with BUSI ultrasound images seeks to achieve accurate and reliable results, underscoring the critical role of advanced image processing in modern healthcare. The developed model demonstrates potential for real-world deployment, particularly in web-based systems, enabling healthcare professionals to detect breast cancer early. Users can seamlessly upload BUSI breast ultrasound images for analysis, with the model providing predictions regarding the presence or absence of breast cancer. This integrated approach not only enhances diagnostic accuracy but also expedites patient care, exemplifying the indispensable role of OpenCV in modern healthcare applications.Keyphrases: accuracy, breast cancer detection, data set, diagnosis, healthcare, mobilenetv2, pre processing, ultrasound image In: Rajakumar G (editor). Proceedings of 6th International Conference on Smart Systems and Inventive Technology, vol 19, pages 244-254.
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