International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 10 Issue: 06 | Jun 2023
p-ISSN: 2395-0072
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Comparative Study of Pre-Trained Neural Network Models in Detection of Glaucoma Pranay Jain1, Sachin Mourya2, Dr. Dhananjay Theckedath3 1-2Dept. of Biomedical Engineering, Thadomal Shahani Engineering College, Mumbai, India
3Associate Professor, Dept. of Electronics and Telecommunication Engineering, Thadomal Shahani Engineering
College, Mumbai, India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - Glaucoma is one of the major causes of blindness
viz. Inception, Xception, ResNet50, MobileNetV3, DenseNet121, and DenseNet169. The Accuracy and Loss Graph of each model was analyzed and along with several other parameters obtained with the help of confusion matrix values were used for the classification of models.
in people all over the globe. If detected quickly, its progression can be slowed, stopped & even permanent blindness can be prevented [1,2]. Several automated methods have come up for the early detection of glaucoma using various artificial neural networking models. In this paper, we present a comparative study of various pre-trained neural network models for the early detection of glaucoma. Six pre-trained models were built and analyzed with the help of several parameters for their comparison.
2. Requirements A labelled database that contains 9690 fundus images from both eyes classified into two classes namely glaucomatous(3422 images) and non-glaucomatous(6268 images), Google Cloud Platform (GCP) – for Cloud Computing, Jupyter notebook - Interactive Python Notebook, Tensorflow – Open source machine learning library for creating and training the models, Keras – Deep learning API for the artificial neural network which runs on top of Tensorflow, NumPy – Python library for high-level mathematical calculations on arrays, Pandas - tool for data manipulation and analysis, Matplotlib – python library that can create animated, static and interactive data visualizations, Seaborn – library for Data Visualization.
Key Words: Glaucoma, Neural Network, Convolutional Neural Network, Transfer Learning, Deep Learning, PreTrained Model.
1.INTRODUCTION Glaucoma is considered one of the leading causes of blindness all over the globe. According to a study by WHO (World Health Organisation), more than 65 million people have been affected by glaucoma throughout the globe. The characteristic feature of glaucoma is a damaged optic nerve due to a rise in intraocular pressure. It is an irreversible neurodegenerative disease. However, its early detection can help in treatment to slow or stop its progression [3].
3. METHODOLOGY The steps for building the classification model are mentioned below.
Glaucoma is generally detected by an ophthalmologist using a set of eye-test known as comprehensive eye test which includes – Tonometry, Pachymetry, Perimetry, Dilated Eye Test, and Gonioscopy. These tests must be done on a regular basis & can be tedious for the ophthalmologist conducting it. A skilled ophthalmologist usually takes an average of about 8 minutes for conducting the test & therefore a computer-aided system is a necessity. Various automated techniques are being developed nowadays for quick & accurate examinations which use neural networking models for the identification of the disease. Fundus scans of an eye can be used to feed the neural network model to detect whether the eye under consideration is glaucomatous or non-glaucomatous.
1.1 Data Pre-Processing The initial setup is the localization of the optic nerve head (ONH) including locating other anatomical structures like blood vessels, tracking, and registering changes within the optic disc region, etc. The input images were first preprocessed for the removal of outliners. Initially, the unnecessary part of the image was cropped. Then several filters were applied to the cropped image to extract useful information [4]. A given dataset may contain images of different dimensions and parameters which may hamper the performance of the neural networking model. Hence, an image data generator which is a library of Keras is used to remove the differences between the individual images in a dataset. It sets the dimensions of images to the given value so that the differences are removed. We have set the resolution to 224 x 224 which is generally used for pre-
As there are many neural network models for image classification, it may be difficult to compare each one and choose the better one for glaucoma detection. In this paper, we have classified six pre-trained neural networking models
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