AIMC Topic: Multilayer Perceptrons

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U-shaped network combining dual-stream fusion mamba and redesigned multilayer perceptron for myocardial pathology segmentation.

Medical physics
BACKGROUND: Cardiac magnetic resonance imaging (CMR) provides critical pathological information, such as scars and edema, which are vital for diagnosing myocardial infarction (MI). However, due to the limited pathological information in single-sequen...

Breast cancer detection and classification: A study on the specification and implementation of multilayer perceptron analog artificial neural networks.

Computers in biology and medicine
Breast cancer is a leading cause of mortality worldwide. Screening therefore remains the best defense against this disease, highlighting the need for accurate and efficient diagnostic methods. Previous authors addressed this issue by implementing dig...

A novel vessel enhancement method based on Hessian matrix eigenvalues using multilayer perceptron.

Bio-medical materials and engineering
BACKGROUND: Vessel segmentation is a critical aspect of medical image processing, often involving vessel enhancement as a preprocessing step. Existing vessel enhancement methods based on eigenvalues of Hessian matrix face challenges such as inconsist...

Radial SERS acquisition on coffee ring for Serum-based breast cancer diagnosis through Multilayer Perceptron.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
The coffee-ring effect, involving spontaneous solute separation, has demonstrated promising potential in the context of patient serum analysis. In this study, an approach leveraging the coffee-ring-based analyte redistribution was developed for spect...

ST-GMLP: A concise spatial-temporal framework based on gated multi-layer perceptron for traffic flow forecasting.

Neural networks : the official journal of the International Neural Network Society
The field of traffic forecasting has been the subject of considerable attention as a critical component in alleviating traffic congestion and improving urban services. Given the regular patterns of human activities, it is evident that traffic flow is...

Improved fractional-order gradient descent method based on multilayer perceptron.

Neural networks : the official journal of the International Neural Network Society
The fractional-order gradient descent (FOGD) method has been employed by numerous scholars in Artificial Neural Networks (ANN), with its superior performance validated both theoretically and experimentally. However, current FOGD methods only apply fr...

Discovering effect of intuitionistic fuzzy transformation in multi-layer perceptron for heart disease prediction: a study.

Computer methods in biomechanics and biomedical engineering
Cardiovascular disease (CVD) is the one of the most fatal diseases in the world we have seen in last two decades. For heart disease detection, imprecision in clinical parameters may occur due to error in taking readings or in measuring devices or env...

Structural network measures reveal the emergence of heavy-tailed degree distributions in lottery ticket multilayer perceptrons.

Neural networks : the official journal of the International Neural Network Society
Artificial neural networks (ANNs) were originally modeled after their biological counterparts, but have since conceptually diverged in many ways. The resulting network architectures are not well understood, and furthermore, we lack the quantitative t...

The combined multilayer perceptron and logistic regression (MLP-LR) method better predicted the spread of Hyphantria cunea (Lepidoptera: Erebidae).

Journal of economic entomology
Hyphantria cunea (Lepidoptera: Erebidae) is one of the pests that pose a serious threat to forest and agronomic crops in China. Its spread is influenced by various factors, including environmental factors and anthropogenic factors, and the available ...

SSA-sMLP: A venous thromboembolism risk prediction model using separable self-attention and spatial-shift multilayer perceptrons.

Thrombosis research
Accurate risk assessment of Venous Thromboembolism (VTE) holds significant value for clinical decision-making. However, traditional scoring systems relying on linear assumptions and expert experience, along with machine learning models constrained by...