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Related Experiment Video

Updated: Jan 25, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework

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Published on: July 22, 2025

Alcoholism Identification Based on an AlexNet Transfer Learning Model.

Shui-Hua Wang^1,2,3, Shipeng Xie⁴, Xianqing Chen⁵

¹School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, China.

Frontiers in Psychiatry

|April 30, 2019

Summary

This summary is machine-generated.

This study introduces an AI approach using AlexNet for alcoholism identification from brain MRIs. The method achieved high accuracy, aiding radiologists in diagnosing alcoholism.

Keywords:

AlexNet alcoholism convolutional neural network data augmentation dropout local response normalization magnetic resonance imaging transfer learning

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Area of Science:

Medical Imaging
Artificial Intelligence
Radiology

Background:

Alcoholism poses significant health risks, necessitating accurate diagnostic tools.
Radiologists require efficient methods for identifying alcoholism in patient scans.

Purpose of the Study:

To develop a novel artificial intelligence (AI) approach for alcoholism identification.
To enhance radiologists' diagnostic capabilities in routine patient screening.

Main Methods:

Utilized AlexNet as a foundational transfer learning model.
Implemented transfer learning with specific learning rate configurations.
Evaluated five distinct transfer learning replacement strategies.

Main Results:

Replacing the final fully connected layer yielded optimal performance.
Achieved high diagnostic metrics: 97.44% sensitivity, 97.41% specificity, 97.34% precision, 97.42% accuracy, and 97.37% F1 score.
Demonstrated robust performance on the test dataset.

Conclusions:

The proposed AI method effectively assists radiologists in alcoholism screening.
Brain magnetic resonance imaging (MRI) analysis using this approach shows promise for early detection.