An integrated deep learning framework using adaptive enhanced vision fusion and modified mobilenet architecture for precision classification of skin diseases with enhanced diagnostic performance
Due to challenges such as illumination variability, noise, and visual distortions, machine learning (ML) and deep learning (DL) approaches for skin disease evaluation remain complex. Traditional methods often neglect these issues, leading to skewed predictions and poor performance. This research lev...
Saved in:
| Main Authors: | Ahsan Bilal Tariq, Muhammad Zaheer Sajid, Nauman Ali khan, Muhammad Fareed Hamid, Anwaar UlHaq, Jarrar Amjad |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-10-01
|
| Series: | SLAS Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2472630325000895 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Method for fetal ultrasound image classification using pseudo-labelling with PCA-KMeans and an attention-augmented MobileNet-LSTM model
by: Aniket K. Shahade, et al.
Published: (2025-12-01) -
Optimized DenseNet Architectures for Precise Classification of Edible and Poisonous Mushrooms
by: Jay Prakash Singh, et al.
Published: (2025-06-01) -
Enhanced DL-Based Breast Cancer Diagnosis and Classification Using Modified DenseNet-121, DenseNet-201, and MobileNetV2: Optimized Architectures and Refined Activation Functions
by: Khaddouj Taifi, et al.
Published: (2025-01-01) -
Deep Learning-Enhanced Ultrasound Analysis: Classifying Breast Tumors Using Segmentation and Feature Extraction
by: Ali Hamza, et al.
Published: (2025-01-01) -
An Improved Neural Network Model Based on DenseNet for Fabric Texture Recognition
by: Li Tan, et al.
Published: (2024-12-01)