  In healthcare, accurately classifying medical images is vital, but
conventional methods often hinge on medical data with a consistent grid
structure, which may restrict their overall performance. Recent medical
research has been focused on tweaking the architectures to attain better
performance without giving due consideration to the representation of data. In
this paper, we present a novel approach for transforming grid based data into
its higher dimensional representations, leveraging unstructured point cloud
data structures. We first generate a sparse point cloud from an image by
integrating pixel color information as spatial coordinates. Next, we construct
a hypersurface composed of points based on the image dimensions, with each
smooth section within this hypersurface symbolizing a specific pixel location.
Polygonal face construction is achieved using an adjacency tensor. Finally, a
dense point cloud is generated by densely sampling the constructed
hypersurface, with a focus on regions of higher detail. The effectiveness of
our approach is demonstrated on a publicly accessible brain tumor dataset,
achieving significant improvements over existing classification techniques.
This methodology allows the extraction of intricate details from the original
image, opening up new possibilities for advanced image analysis and processing
tasks.

Computer Vision and Pattern Recognition;Machine Learning