Segmentation of Chest Radiographs for Tuberculosis Screening Using Kernel Mapping and Graph Cuts

Fatima, Ayesha; Tariq, Anam; Akhtar, Mahmood; Zahid, Hina

doi:10.1007/978-3-319-96139-2_3

Cited by 1 publication

(1 citation statement)

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“…Some methods used binary information, and some used regional information, but graph cut utilized both binary and regional information [7]. Another algorithm, in which contour was presented as a polygon with fixed labeled points.…”

Section: Asmmentioning

confidence: 99%

In Shortly about Neural Networks

Franjić¹,

Galić²

2021

ACC

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A neural network is a collection of neurons that are interconnected and interactive through signal processing operations. The traditional term "neural network" refers to a biological neural network, i.e., a network of biological neurons. The modern meaning of this term also includes artificial neural networks, built of artificial neurons or nodes. Machine learning includes adaptive mechanisms that allow computers to learn from experience, learn by example and by analogy. Learning opportunities can improve the performance of an intelligent system over time. One of the most popular approaches to machine learning is artificial neural networks. An artificial neural network consists of several very simple and interconnected processors, called neurons, which are based on modeling biological neurons in the brain. Neurons are connected by calculated connections that pass signals from one neuron to another. Each connection has a numerical weight associated with it. Weights are the basis of long-term memory in artificial neural networks. They express strength or importance for each neuron input. An artificial neural network "learns" through repeated adjustments of these weights.

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Section: Asmmentioning

confidence: 99%