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Updated: May 22, 2025

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

An Integrated Deep Learning Model with EfficientNet and ResNet for Accurate Multi-Class Skin Disease Classification.

Madallah Alruwaili¹, Mahmood Mohamed²

¹Department of Computer Engineering and Networks, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Aljouf, Saudi Arabia.

Diagnostics (Basel, Switzerland)

|March 13, 2025

Summary

This summary is machine-generated.

A novel deep learning model combining three CNNs achieves 99.14% accuracy for skin disease classification. This fusion-level approach enhances diagnostic stability and precision for conditions like skin cancer.

Keywords:

EfficientNet ResNet dermatological image analysis fusion-based deep learning multi-class classification skin disease classification

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Last Updated: May 22, 2025

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Area of Science:

Dermatology
Artificial Intelligence
Medical Imaging

Background:

Medical diagnosis of skin diseases is challenging due to patient variations.
Accurate classification of conditions like skin cancer and neoplasms is critical.

Purpose of the Study:

To develop a stable, high-performance deep learning model for skin disease classification.
To improve diagnostic accuracy through a fusion-level approach.

Main Methods:

A fusion-level deep learning model merging EfficientNet-B0, EfficientNet-B2, and ResNet50 was designed.
The model extracts features using distinct CNN branches and a fusion mechanism.
The Kaggle Skin Diseases Image Dataset (27,153 images) was used for training, validation, and testing.

Main Results:

The proposed model achieved 99.14% accuracy.
Excellent precision, recall, and F1-score metrics were obtained.
The model demonstrated high classification precision.

Conclusions:

The deep learning model shows significant potential for automated dermatological diagnosis.
The approach is promising for clinical applications in skin disease classification.