  An innovative two-stage methodology for categorizing blood clot origins is
presented in this paper, which is important for the diagnosis and treatment of
ischemic stroke. First, a background classifier based on MobileNetV3 segments
big whole-slide digital pathology images into numerous tiles to detect the
presence of cellular material. After that, different pre-trained image
classification algorithms are fine-tuned to determine the origin of blood
clots. Due to complex blood flow dynamics and limitations in conventional
imaging methods such as computed tomography (CT), magnetic resonance imaging
(MRI), and ultrasound, identifying the sources of blood clots is a challenging
task. Although these techniques are useful for identifying blood clots, they
are not very good at determining how they originated. To address these
challenges, our method makes use of robust computer vision models that have
been refined using information from whole-slide digital pathology images. Out
of all the models tested, the PoolFormer \cite{yu2022metaformer} performs
better than the others, with 93.4\% accuracy, 93.4\% precision, 93.4\% recall,
and 93.4\% F1-score. Moreover, it achieves the good weighted multi-class
logarithmic loss (WMCLL) of 0.4361, which emphasizes how effective it is in
this particular application. These encouraging findings suggest that our
approach can successfully identify the origin of blood clots in a variety of
vascular locations, potentially advancing ischemic stroke diagnosis and
treatment approaches.

Image and Video Processing;Computer Vision and Pattern Recognition