A Multi-Modal Deep Learning Framework for On-Device Medical Image Analysis with Augmented Reality Visualization

DOI

10.2991/978-94-6239-707-1_26How to use a DOI?

Keywords

Medical Imaging; Edge AI; Augmented Reality; Quantization; YOLOv8; Multi-modal Diagnosis

Abstract

This paper introduces a consolidated, edge-native architecture engineered to evaluate four distinct medical imaging modalities—retinal fundus photographs, dermatoscopic lesions, thoracic X-rays, and cranial MRI—directly on consumer-grade mobile hardware. Rather than relying on cloud connectivity, we operationalize compact neural networks (such as YOLOv8 and EfficientNet derivatives) locally on Android platforms. By employing post-training quantization pipelines under the TensorFlow Lite ecosystem, the framework drastically trims structural memory footprints and execution delays without compromising predictive fidelity. Furthermore, a synchronous augmented reality interface projects diagnostic determinations onto three-dimensional anatomical reference markers. This localized, cloud-free methodology guarantees strict data confidentiality and near-zero latency, offering a highly practical screening instrument for environments suffering from infrastructural deficits.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Atharva R. Awade
AU  - Deepak D. Kshirsagar
PY  - 2026
DA  - 2026/06/18
TI  - A Multi-Modal Deep Learning Framework for On-Device Medical Image Analysis with Augmented Reality Visualization
BT  - Proceedings of the International Conference on Recent Advances in Intelligent and Sustainable Technologies (RAIST 2026)
PB  - Atlantis Press
SP  - 299
EP  - 308
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6239-707-1_26
DO  - 10.2991/978-94-6239-707-1_26
ID  - Awade2026
ER  -