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Updated: Jul 16, 2026

A 3D Spheroid Model for Glioblastoma
Published on: April 9, 2020
Neurocomputing model for computation of an approximate convex hull of a set of points and spheres
Abstract:
In this letter, a two-layer neural network is proposed for computation of an approximate convex hull of a set of given points in 3-D or a set of spheres of different sizes. The algorithm is designed based on an elegant concept-shrinking of a spherical rubber balloon surrounding the set of objects in 3-D. Logically, a set of neurons is orderly placed on a spherical mesh i.e., on a rubber balloon surrounding the objects. Each neuron has a parameter vector associated with its current position. The resultant force of attraction between a neuron and each of the given points/objects, determines the direction of a movement of the neuron lying on the rubber balloon. As the network evolves, the neurons (parameter vectors) approximate the convex hull more and more accurately.
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