Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
Access to medical screening remains a critical challenge in rural and resource-limited communities where specialist infrastructure is scarce. This paper presents an Android-based offline mobile application integrating retinal disease detection, dermatological classification, and colour vision deficiency assessment in a single platform. MobileNetV2 and EfficientNetB3 models, trained on publicly available Kaggle datasets through transfer learning, achieve 97% classification accuracy. A two-stage validation pipeline comprising domain gating and confidence thresholding filters invalid inputs before inference. Built with React Native and TensorFlow Lite, all processing executes entirely on-device without internet connectivity, making accurate multi-domain clinical screening accessible to underserved populations worldwide.
Keywords:
Mobile health, deep learning, diabetic retinopathy, skin disease classification, colour vision testing, TensorFlow Lite, MobileNetV2, EfficientNetB3, offline inference, transfer learning.
Cite Article:
"Android-Based Offline Eye and Skin Disease Detection Application With Visual Acuity Testing", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 4, page no.a599-a604, April-2026, Available :http://www.ijrti.org/papers/IJRTI2604084.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator