Enhancing Malignant Lymph Node Detection in Ultrasound Imaging: A Comparison Between the Artificial Intelligence Accuracy, Dice Similarity Coefficient and Intersection over Union

Background: The accurate identification of malignant lymph nodes in cervical ultrasound images is crucial for early diagnosis and treatment planning. Traditional evaluation metrics, such as accuracy and the Dice Similarity Coefficient (DSC), often fail to provide a realistic assessment of segmentation performance, as they do not account for partial overlaps between predictions and ground truth. This study addresses this gap by introducing the Intersection over Union (IoU) as an additional metric to offer a more comprehensive evaluation of model performance. Specifically, we aimed to develop a convolutional neural network (CNN) capable of detecting suspicious malignant lymph nodes and assess its effectiveness using both conventional and IoU-based performance metrics. Methods: A dataset consisting of 992 malignant lymph node images was extracted from 166 cervical ultrasound scans and labeled using the ImgLab annotation tool. A CNN was developed using Python, Keras, and TensorFlow and employed within the Jupyter Notebook environment. The network architecture consists of four neural layers trained to distinguish malignant lymph nodes. Results: The CNN achieved a training accuracy of 97% and a validation accuracy of 99%. The DSC score was 0.984, indicating a strong segmentation performance, although it was limited to detecting malignant lymph nodes in positive cases. An IoU evaluation applied to the test images revealed an average overlap of 74% between the ground-truth labels and model predictions, offering a more nuanced measure of the segmentation accuracy. Conclusions: The CNN demonstrated high accuracy and DSC scores, confirming its effectiveness in identifying malignant lymph nodes. However, the IoU values, while lower than conventional accuracy metrics, provided a more realistic evaluation of the model’s performance, highlighting areas for potential improvement in segmentation accuracy. This study underscores the importance of using IoU alongside traditional metrics to obtain a more reliable assessment of deep learning-based medical image analysis models.