An Improved Liver Disease Detection Based on YOLOv8 Algorithm

The identification and diagnosis of liver diseases hold significant importance within the domain of digital pathology research. Various methods have been explored in the literature to address this crucial task, with deep learning techniques emerging as particularly promising due to their ability to yield highly accurate results compared to other traditional approaches. However, despite these advancements, a significant research gap persists in the field. Many deep learning-based liver disease detection methods continue to struggle with achieving consistently high accuracy rates. This issue is highlighted in numerous studies where traditional convolutional neural networks and hybrid models fall short in precision and recall metrics. To bridge this gap, our study proposes a novel approach utilizing the YOLOv8 algorithm, which is designed to significantly enhance the accuracy and effectiveness of liver disease detection. The YOLOv8 algorithm's architecture is well-suited for real-time object detection and has been optimized for medical imaging applications. Our method involves generating innovative models tailored specifically for liver disease detection by leveraging a comprehensive dataset from the Roboflow repository, consisting of 3,976 annotated liver images. This dataset provides a diverse range of liver disease cases, ensuring robust model training. Our approach includes meticulous model training with rigorous hyperparameter tuning, using 70% of the data for training, 20% for validation, and 10% for testing. This structured training process ensures that the model learns effectively while minimizing overfitting. We evaluate the model using precision, recall, and mean average precision (mAP@0.5) metrics, demonstrating significant improvements over existing methods. Through extensive experimental results and detailed performance evaluations, our study achieves high accuracy rates, thus addressing the existing research gap and providing an effective approach for liver disease detection.

@article{Huang2024,
  title     = {An Improved Liver Disease Detection Based on YOLOv8 Algorithm},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {7},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Junjie Huang and Caihong Li and Fengjun Yan and Yuanchun Guo},
  doi       = {10.14569/IJACSA.2024.01507114},
  url       = {https://doi.org/10.14569/IJACSA.2024.01507114}
}