Author(s):
1. Aroosa Yaqoob:
Department of Computer Science, Abasyn University Islamabad, Pakistan
2. Abdul Basit:
Department of Computer Science, Abasyn University Islamabad, Pakistan
3. Sibtul Hassan:
Department of Computer Science, Abasyn University Islamabad, Pakistan
4. Abdul Hannan:
Department of Computer Science, Abasyn University Islamabad, Pakistan
Abstract:
The annual incidence of liver cancer is rising, which emphasizes how important it is to accurately identify liver tumors in order to inform treatment plans. Liver tumor delineation has traditionally been accomplished by tedious manual labeling, which is an arbitrary technique prone to error. Computer-based liver tumor segmentation techniques have gained popularity as a solution to this problem. However, because tumor sizes, shapes, and picture intensities vary widely, segmentation is still difficult. In this work, we provide a new method called Liver tumor segmentation using Vision Transformer (ViT), which seamlessly integrates spatial data in place of conventional skip-connections. ViT uses multihead self-attention to operate on low-resolution feature maps and reworks the output into a feature map for concatenation later on. For liver tumor segmentation, we use a Kaggle dataset of CT images that is accessible to the public. Our suggested vision transformer-based model shows excellent accuracy; it can diagnose liver tumors from normal liver CT images with 89% accuracy. The findings of this study may help physicians, radiologists, and practitioners diagnose liver tumors in their early stages with greater accuracy and predictability.
Page(s):
1-1
DOI:
DOI not available
Published:
Journal: Second International Conference on Computing Technologies, Tools and Applications (ICTAPP-24), June 4-6,2024 (Abstract Book), Volume: 0, Issue: 0, Year: 2024
Keywords:
Liver Cancer
,
segmentation
,
Liver Tumors
,
Vision Transformer
,
Deep Learning
,
CTImages
References:
References are not available for this document.
Citations
Citations are not available for this document.