Multimodal Deep Learning Framework Combines Image and Clinical Data for Accurate Skin Disease Prediction

Sathyamangalam, India - December 29, 2025 - A newly published peer-reviewed research study presents a multimodal deep learning framework that integrates dermatological images with clinical data to improve the accuracy of skin disease prediction, offering significant potential for AI-assisted diagnosis and clinical decision-support systems.

The research article, titled "Multimodal Deep Learning Framework Combining Image and Clinical Data for Accurate Skin Disease Prediction [https://tianjindaxuexuebao.com/details.php?id=DOI:10.5281/zenodo.17877457]," introduces an advanced artificial intelligence approach designed to address key limitations of conventional single-modality diagnostic models. The study is now publicly available through an international academic journal platform.

Accurate diagnosis of skin diseases often requires the combined interpretation of visual symptoms and patient clinical information. Traditional automated approaches frequently rely on image data alone, which may limit predictive reliability. The proposed framework overcomes this challenge by integrating convolutional neural networks for image feature extraction with structured clinical data analysis.

According to the study, the multimodal model demonstrated improved classification accuracy and robustness compared with models based solely on either image data or clinical parameters. By leveraging heterogeneous data sources, the framework enhances feature representation and supports more consistent disease prediction outcomes. The research methodology involved image preprocessing, normalization of clinical attributes, multimodal feature fusion, and extensive model evaluation using standard performance metrics. Findings indicate strong applicability in teledermatology, early disease screening, and intelligent clinical decision-support tools.

The full research article can be accessed at: https://tianjindaxuexuebao.com/details.php?id=DOI:10.5281/zenodo.17877457

About the Authors

Nivedha S is an Assistant Professor in the Department of Information Technology at Bannari Amman Institute of Technology, Sathyamangalam, Tamil Nadu, India. Her academic interests include artificial intelligence, machine learning, and data-driven application development.

Biju J is an Assistant Professor in the Division of Data Science and Cyber Security at Karunya Institute of Technology and Sciences, Coimbatore, India. His research areas include deep learning, data analytics, cybersecurity, and applied data science.

Sathyaraj S is an Assistant Professor in the Department of Artificial Intelligence and Data Science at NPR College of Engineering and Technology, Natham, Dindigul, Tamil Nadu, India. His research interests include medical data analysis, machine learning, and intelligent decision-support systems.

Parthasarathi P is an Associate Professor in the Department of Computer Science and Engineering at Bannari Amman Institute of Technology, Sathyamangalam, Tamil Nadu, India. His expertise includes artificial intelligence, data science, and advanced computing methodologies.
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Website: https://tianjindaxuexuebao.com/details.php?id=DOI:10.5281/zenodo.17877457

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