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Cataract and glaucoma detection based on Transfer Learning using MobileNet.

Sheikh Muhammad Saqib1, Muhammad Iqbal2, Muhammad Zubair Asghar2

  • 1Department of Computing and Information Technology, Gomal University, D.I.Khan 29050, Pakistan.

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This summary is machine-generated.

Early detection of eye diseases like cataracts and glaucoma is crucial for preventing blindness. This study introduces optimized MobileNet models for accurate, automated disease detection, outperforming existing methods.

Keywords:
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Area of Science:

  • Ophthalmology
  • Computer Science
  • Artificial Intelligence

Background:

  • Cataracts and glaucoma are leading causes of blindness.
  • Early and accurate diagnosis is vital for effective treatment and risk reduction.
  • Machine learning and deep learning show promise for automated eye disease detection.

Purpose of the Study:

  • To develop and evaluate lightweight deep neural networks for the automatic detection of cataracts and glaucoma.
  • To compare the performance of proposed MobileNetV1 and MobileNetV2 models against other deep learning architectures.

Main Methods:

  • Utilized depth-wise separable convolutions to build optimized, lightweight deep neural networks.
  • Employed MobileNetV1 and MobileNetV2 architectures.
  • Trained and tested models on publicly available datasets of cataract/normal and glaucoma/normal images.

Main Results:

  • The proposed MobileNetV1 and MobileNetV2 models achieved the highest accuracy in detecting both cataracts and glaucoma.
  • The optimized architecture demonstrated superior performance compared to other evaluated deep learning models.

Conclusions:

  • Lightweight deep neural networks, specifically optimized MobileNet architectures, are highly effective for automated cataract and glaucoma detection.
  • This approach offers a promising tool for early diagnosis and prevention of blindness caused by these eye conditions.