Critical retinal disease detection from optical coherence tomography images by deep convolutional neural network and explainable machine learning

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.

Bibliografiske detaljer
Main Authors:	Datta, Pranab, Islam, Saniul, Das, Retuparna, Zabir, Mihiran Uddin
Andre forfattere:	Alam, Md. Golam Rabiul
Format:	Thesis
Sprog:	English
Udgivet:	Brac University 2021
Fag:	Machine Learning Image classification Artificial Intelligence Optical Coherence Tomography CNN Inception V3 Resnet50 VGG16 Xception Black Box LRP
Online adgang:	http://hdl.handle.net/10361/15762

id	10361-15762
record_format	dspace
spelling	10361-157622022-01-26T10:18:16Z Critical retinal disease detection from optical coherence tomography images by deep convolutional neural network and explainable machine learning Datta, Pranab Islam, Saniul Das, Retuparna Zabir, Mihiran Uddin Alam, Md. Golam Rabiul Department of Computer Science and Engineering, Brac University Machine Learning Image classification Artificial Intelligence Optical Coherence Tomography CNN Inception V3 Resnet50 VGG16 Xception Black Box LRP Machine Learning This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. Cataloged from PDF version of thesis. Includes bibliographical references (pages 49-52). Retinal disease diagnosis by machine learning can be achieved using Deep Neural Network based predictors. Use of Explainable Artificial Intelligence (XAI) has the potential to explain the black box of those neural network models which are used in identifying critical retinal diseases. Due to lack of explanation in neural networks, Machine Learning based systems are not well trusted in medical field. People still have to rely on the doctor’s clarification to come into any conclusion with medical issues. In our proposed model, we have used several Convolutional Neural Net work (CNN) models leveraging transfer learning to identify some of the critical reti nal diseases such as Choroidal Neovascularization (CNV), Diabetic Macular Edema (DME), DRUSEN from Optical Coherence Tomography (OCT) images along with the explanation. For our classification task, we have used four different CNN models namely which are ResNet 50, inceptionv3, xception, VGG 16. At first, A dataset of several thousand OCT images consisting of four classes: CNV, DME, DRUSEN, and Normal were collected. Afterward, The dataset were pre-processed and applied to our proposed CNN models to classification. We achieved the accuracy gradually 95.20 %, 94.00 %, 96.30 % and 93.30 % by performing the four deep learning model respectively Inception V3, ResNet50, VGG16, and Xception. Eventually, in order to understand the results produced by the black box models, we applied a method of Explainable AI named Layer-wise Propagation (LRP) for a better understanding of retinal disease detection by the CNN models. To add with, the LRP have analysed the models with back propagation and focused on the area of the input image based on the model’s training parameters. To sum up, our proposed model has been able to perform critical retinal diseases detection as well as the explanation behind the identification. Pranab Datta Md. Saniul Islam Retuparna Das Mihiran Uddin Zabir B. Computer Science 2021-12-26T06:36:28Z 2021-12-26T06:36:28Z 2021 2021-01 Thesis ID 16301177 ID 16301020 ID 16301205 ID 16301198 http://hdl.handle.net/10361/15762 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 52 Pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Machine Learning Image classification Artificial Intelligence Optical Coherence Tomography CNN Inception V3 Resnet50 VGG16 Xception Black Box LRP Machine Learning
spellingShingle	Machine Learning Image classification Artificial Intelligence Optical Coherence Tomography CNN Inception V3 Resnet50 VGG16 Xception Black Box LRP Machine Learning Datta, Pranab Islam, Saniul Das, Retuparna Zabir, Mihiran Uddin Critical retinal disease detection from optical coherence tomography images by deep convolutional neural network and explainable machine learning
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.
author2	Alam, Md. Golam Rabiul
author_facet	Alam, Md. Golam Rabiul Datta, Pranab Islam, Saniul Das, Retuparna Zabir, Mihiran Uddin
format	Thesis
author	Datta, Pranab Islam, Saniul Das, Retuparna Zabir, Mihiran Uddin
author_sort	Datta, Pranab
title	Critical retinal disease detection from optical coherence tomography images by deep convolutional neural network and explainable machine learning
title_short	Critical retinal disease detection from optical coherence tomography images by deep convolutional neural network and explainable machine learning
title_full	Critical retinal disease detection from optical coherence tomography images by deep convolutional neural network and explainable machine learning
title_fullStr	Critical retinal disease detection from optical coherence tomography images by deep convolutional neural network and explainable machine learning
title_full_unstemmed	Critical retinal disease detection from optical coherence tomography images by deep convolutional neural network and explainable machine learning
title_sort	critical retinal disease detection from optical coherence tomography images by deep convolutional neural network and explainable machine learning
publisher	Brac University
publishDate	2021
url	http://hdl.handle.net/10361/15762
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Critical retinal disease detection from optical coherence tomography images by deep convolutional neural network and explainable machine learning

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