Lightweight Detection Algorithm for Breast-Mass Features in Ultrasound Images

Aim:

        The project aims to design a lightweight, high-precision breast-mass detection framework using YOLOv11 that can accurately identify lesions in ultrasound images. It seeks to reduce false detections and enable real-time performance on medical imaging systems.

Abstract:

         This project introduces an advanced breast-mass detection framework utilizing the YOLOv11 deep learning model to analyze ultrasound images with high accuracy and reliability. The system is designed to overcome challenges such as speckle noise, low contrast, and irregular lesion shapes commonly found in ultrasound imaging. YOLOv11’s improved multiscale feature extraction substantially enhances the detection of small and complex tumor regions. The model automatically identifies and localizes malignant and benign masses with faster processing and fewer false detections compared to earlier YOLO versions. A curated medical dataset is used for training and evaluation to ensure clinical relevance and robustness. The system incorporates optimized image preprocessing and real-time prediction capabilities to support medical practitioners. Experimental results demonstrate strong performance in precision, recall, and boundary accuracy. The lightweight structure of YOLOv11 allows deployment on clinical devices with limited computational resources. The proposed framework provides a practical and efficient solution for early breast cancer screening. Overall, it contributes to improving diagnostic workflows and enhancing patient care.

Proposed System:

       The proposed system utilizes the YOLOv11 deep learning model to accurately detect and classify breast masses from ultrasound images in real time. YOLOv11 is chosen due to its enhanced feature extraction ability, improved multi-scale detection performance, and high processing speed, which make it suitable for medical imaging environments. In this system, ultrasound images are preprocessed and fed into the YOLOv11 architecture, where the model extracts important spatial features and identifies potential tumor regions. YOLOv11’s upgraded backbone and detection head improve small-lesion detection, reduce false positives, and handle noise commonly present in ultrasound data.

Advantage:

1. Improved detection accuracy.
2. Enhanced real-time performance suitable for practical applications.
3. Lightweight and efficient model design for ease of deployment.

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