Department of Computer and Software Engineering, National University of Sciences and Technology, Islamabad, 44000, Pakistan.
Sci Rep. 2024 Oct 8;14(1):23489. doi: 10.1038/s41598-024-71080-4.
Automated segmentation of biomedical image has been recognized as an important step in computer-aided diagnosis systems for detection of abnormalities. Despite its importance, the segmentation process remains an open challenge due to variations in color, texture, shape diversity and boundaries. Semantic segmentation often requires deeper neural networks to achieve higher accuracy, making the segmentation model more complex and slower. Due to the need to process a large number of biomedical images, more efficient and cheaper image processing techniques for accurate segmentation are needed. In this article, we present a modified deep semantic segmentation model that utilizes the backbone of EfficientNet-B3 along with UNet for reliable segmentation. We trained our model on Non-melanoma skin cancer segmentation for histopathology dataset to divide the image in 12 different classes for segmentation. Our method outperforms the existing literature with an increase in average class accuracy from 79 to 83%. Our approach also shows an increase in overall accuracy from 85 to 94%.
生物医学图像的自动分割已被公认为计算机辅助诊断系统中用于检测异常的重要步骤。尽管如此，由于颜色、纹理、形状多样性和边界的变化，分割过程仍然是一个开放的挑战。语义分割通常需要更深的神经网络来实现更高的准确性，这使得分割模型更加复杂和缓慢。由于需要处理大量的生物医学图像，因此需要更有效和更便宜的图像处理技术来实现准确的分割。在本文中，我们提出了一种改进的深度语义分割模型，该模型利用 EfficientNet-B3 的主干和 UNet 进行可靠的分割。我们在用于组织病理学数据集的非黑色素瘤皮肤癌分割上对我们的模型进行了训练，以便将图像分为 12 个不同的类别进行分割。我们的方法在平均类精度从 79%提高到 83%的情况下优于现有文献。我们的方法还显示出整体精度从 85%提高到 94%。
