A Novel Fuzzy DBNet for Medical Image Segmentation

When doctors are fatigued, they often make diagnostic errors. Similarly, pharmacists may also make mistakes in dispensing medication. Therefore, object segmentation plays a vital role in many healthcare-related areas, such as symptom analysis in biomedical imaging and drug classification. However, many traditional deep-learning algorithms use a single view of an image for segmentation or classification. When the image is blurry or incomplete, these algorithms fail to segment the pathological area or the shape of the drugs accurately, which can then affect subsequent treatment plans. Consequently, we propose the Fuzzy DBNet, which combines the dual butterfly network and the fuzzy ASPP in a deep-learning network and processes images from both sides of an object simultaneously. Our experiments used multi-category pill and lung X-ray datasets for training. The average Dice coefficient of our proposed model reached 95.05% in multi-pill segmentation and 97.05% in lung segmentation. The results showed that our proposed model outperformed other state-of-the-art networks in both applications, demonstrating that our model can use multiple views of an image to obtain image segmentation or identification.

Automated summary

When doctors and pharmacists are fatigued, they may make errors in diagnostic and medication dispensing. Object segmentation plays a crucial role in healthcare-related areas, but traditional deep-learning algorithms often fail to accurately segment or classify images with blur or incompleteness. Therefore, the proposed Fuzzy DBNet combines the dual butterfly network and fuzzy ASPP to process images from both sides of an object simultaneously, achieving high performance in multi-pill and lung segmentation tasks.