Optimized YOLOv8 for enhanced breast tumor segmentation in ultrasound imaging.
Authors
Affiliations (8)
Affiliations (8)
- Information Systems Department, College of Computer and Information Sciences, Jouf University, Sakaka, 72388, Saudi Arabia. [email protected].
- Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka, 72388, Saudi Arabia.
- Information Systems Department, College of Computer and Information Sciences, Jouf University, Sakaka, 72388, Saudi Arabia.
- Department of Information Systems, MCI Academy, Cairo, Egypt.
- Computer Science Department, Faculty of Computers and Informatics, Zagazig University, Zagazig, 44511, Egypt.
- Computer Science Department, College of Computer Science and Engineering, Taibah University, Medina, 42353, Saudi Arabia.
- Department of Internal Medicine, College of Medicine, Shaqra University, Shaqra, Saudi Arabia.
- Department of Internal Medicine, Faculty of Medicine, Al-Azhar University, Cairo, Egypt.
Abstract
Breast cancer significantly affects people's health globally, making early and accurate diagnosis vital. While ultrasound imaging is safe and non-invasive, its manual interpretation is subjective. This study explores machine learning (ML) techniques to improve breast ultrasound image segmentation, comparing models trained on combined versus separate classes of benign and malignant tumors. The YOLOv8 object detection algorithm is applied to the image segmentation task, aiming to capitalize on its robust feature detection capabilities. We utilized a dataset of 780 ultrasound images categorized into benign and malignant classes to train several deep learning (DL) models: UNet, UNet with DenseNet-121, VGG16, VGG19, and an adapted YOLOv8. These models were evaluated in two experimental setups-training on a combined dataset and training on separate datasets for benign and malignant classes. Performance metrics such as Dice Coefficient, Intersection over Union (IoU), and mean Average Precision (mAP) were used to assess model effectiveness. The study demonstrated substantial improvements in model performance when trained on separate classes, with the UNet model's F1-score increasing from 77.80 to 84.09% and Dice Coefficient from 75.58 to 81.17%, and the adapted YOLOv8 model achieving an F1-score improvement from 93.44 to 95.29% and Dice Coefficient from 82.10 to 84.40%. These results highlight the advantage of specialized model training and the potential of using advanced object detection algorithms for segmentation tasks. This research underscores the significant potential of using specialized training strategies and innovative model adaptations in medical imaging segmentation, ultimately contributing to better patient outcomes.