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Solution to OCT Diagnosis Using Simple Baseline CNN Models and Hyperparameter Tuning

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International Conference on Innovative Computing and Communications

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1394))

Abstract

Aim: Computer-aided diagnosis of retinopathy is a research hotspot in the field of medical image classification. Optical coherence tomography (OCT) is widely applied in the diagnosis of ocular diseases. In this study, we aim to classify the images into four classes, namely CNV, DME, drusen and normal. Methodology: In this study, we present the solution to classify the OCT images using simple baseline 3, 5 and 7 layer deep convolutional neural networks (CNNs). It also explores the effect of hyperparameters such as dropout, image size, batch normalisation, epochs and their relationships with the accuracy, sensitivity and specificity of the models. Results: The novelty of this study is that it does not use any pre-trained models and yet achieves desired results just by hyperparameter tuning and some clever observations. The best results were yielded by 5 layer model having hyperparameters image size 64 × 64, 30 epochs with dropout and batch normalisation achieving an accuracy of 97.92%. The biggest risk of overfitting in deep learning where multiple layered models are trained and tested and approaches of diminishing the overfitting effect has been discussed in detail.

Kushwaha and Rastogi authors have contributed equally to the work.

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Funding

There was no funding provided to carry out this research.

Author information

Authors and Affiliations

  1. Department of Computer Science and IT, Jaypee Institute of Information Technology, Sec-62, Noida, India

    Ajay Kumar Kushwaha & Somil Rastogi

Authors
  1. Ajay Kumar Kushwaha
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  2. Somil Rastogi
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Editor information

Editors and Affiliations

  1. Maharaja Agrasen Institute of Technology, Delhi, India

    Ashish Khanna

  2. Department of Computer Science Engineering, Maharaja Agrasen Institute of Technology, Rohini, Delhi, India

    Deepak Gupta

  3. Rajnagar Mahavidyalaya, Birbhum, India

    Siddhartha Bhattacharyya

  4. Computer and Artificial Intelligence, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  5. Department of Computer Science, Shaheed Sukhdev College of Business Studies, Rohini, Delhi, India

    Sameer Anand

  6. Department of Computer Science, Shaheed Sukhdev College of Business Studies, Rohini, Delhi, India

    Ajay Jaiswal

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Kushwaha, A.K., Rastogi, S. (2022). Solution to OCT Diagnosis Using Simple Baseline CNN Models and Hyperparameter Tuning. In: Khanna, A., Gupta, D., Bhattacharyya, S., Hassanien, A.E., Anand, S., Jaiswal, A. (eds) International Conference on Innovative Computing and Communications. Advances in Intelligent Systems and Computing, vol 1394. Springer, Singapore. https://doi.org/10.1007/978-981-16-3071-2_30

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