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ORIGINAL RESEARCH article

Front. Med.

Sec. Precision Medicine

Volume 12 - 2025 | doi: 10.3389/fmed.2025.1619706

This article is part of the Research TopicIntegrating AI and Machine Learning in Advancing Patient Care: Bridging Innovations in Mental Health and Cognitive NeuroscienceView all 12 articles

Image Steganalysis using LSTM fused Convolutional Neural Networks for Secure Telemedicine

Provisionally accepted
Doaa  ShehabDoaa Shehab*Mohmmed  AlhaddadMohmmed Alhaddad
  • King Abdulaziz University, Jeddah, Makkah, Saudi Arabia

The final, formatted version of the article will be published soon.

Deep learning based image steganalysis has progressed in recent times, with efforts more concerted toward prioritizing detection accuracy over lightweight frameworks. In the context of AI-driven health solutions, ensuring the security and integrity of medical images is imperative. This paper introduces a novel approach that leverages the correlation between local image features using a CNN fused LSTM (Long Short-Term Memory) model for enhanced feature extraction. By replacing the fully connected layers of conventional CNN architectures with LSTM, our proposed method prioritizes high-relevance features, making it a viable choice for detecting hidden data within medical and sensitive imaging datasets. The LSTM layers in our hybrid model demonstrate better sensitivity characteristics for ensuring privacy in AI-driven diagnostics and telemedicine.Experiments were conducted on Break Our Steganographic System (BOSS Base 1.01) and Break Our Watermarking System (BOWS) datasets, followed by validation on the ALASKA2 Image Steganalysis dataset. The results confirm that our approach generalizes effectively and would serve as impetus to ensure security and privacy for digital health care solutions.

Keywords: steganalysis, Steganography, data hiding, healthcare security, LSTM, Lightweight

Received: 28 Apr 2025; Accepted: 31 Jul 2025.

Copyright: © 2025 Shehab and Alhaddad. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Doaa Shehab, King Abdulaziz University, Jeddah, 21589, Makkah, Saudi Arabia

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