Performance Evaluation of the U-Net Model for Medical Image Segmentation Using Dice Coefficient, IOU, and Loss Metrics

The U-Net model for lung area segmentation in medical images is considered in this work. A preprocessed and enhanced dataset was used to train the U-Net model across 70 epochs using an encoder-decoder architecture with skip connections. To estimate the accuracy of the model, important performance indicators such as the Dice coefficient, Intersection over Union (IOU), and training/validation loss were observed. The coefficient for the dice went up from 0.5 to 0.9 in the results, while the IOU value calmed at 0.9, representing the model’s efficiency in proper segmentation. Strong generalization to previously unseen data and minimal overfitting were shown by the loss metrics’ in accordance reduction. In agreement to the study, U-Net has the potential to be used in real-world medical applications. Further study is recommended to enhance performance through the application of transfer learning and consideration devices.