Deep Learning-based Smart IoT Health System for Blindness Detection using Retina Images

Amit Kumar Jaiswal, Prayag Tiwari, Sachin Kumar, Mabrook S. Al-Rakhami, Mubarak Alrashoud, Ahmed Ghoneim

Research output: Contribution to journalArticleScientificpeer-review


Deep Learning-based Smart Healthcare is getting so much attention due to real-time applicability in everyone life’s, and It has obtained more attention with the convergence of IoT. Diabetic eye disease is the primary cause of blindness between working aged peoples. The major populated Asian countries such as India and China presently account for millions of people and at the verge of an eruption of diabetic inhabitants. These growing number of diabetic patients posed a major challenge among trained doctors to provide medical screening and diagnosis. Our goal is to leverage the deep learning techniques to automate the detection of blind spot in an eye and identify how severe the stage may be. In this paper, we propose an optimized technique on top of recently released pre-trained EfficientNet models for blindness identification in retinal images along with a comparative analysis among various other neural network models. Our fine-tuned EfficientNet-B5 based model evaluation follows the benchmark dataset of retina images captured using fundus photography during varied imaging stages and outperforms CNN and ResNet50 models.

Original languageEnglish
JournalIEEE Access
Publication statusE-pub ahead of print - 2021
MoE publication typeA1 Journal article-refereed


  • Blindness
  • CNN
  • Data models
  • Diabetes
  • Diabetic Retinopathy
  • Image resolution
  • IoT
  • Medical Diagnosis
  • Medical services
  • Retina
  • Retina Images
  • Retinopathy


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