Deep learning based automated diagnosis of Ocular Toxoplasmosis in fundus images using convolutional neural network

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2024.

Bibliografske podrobnosti
Main Authors:	Choudhury, Prionto Kumar, Anika, Asma Akter, Ramisa, Sumaiya Rahman, Zaman, Arsi, Chowdhury, Rizvee Rifat
Drugi avtorji:	Alam, Md.Golam Rabiul
Format:	Thesis
Jezik:	English
Izdano:	Brac University 2024
Teme:	Ocular Toxoplasmosis Convolutional neural network MobileNet VGG16 ResNet50 Deep learning VGG19 Neural networks (Computer science) Eye > Diseases Diagnostic imaging Deep learning (Machine learning)
Online dostop:	http://hdl.handle.net/10361/22916

id	10361-22916
record_format	dspace
spelling	10361-229162024-05-26T21:01:29Z Deep learning based automated diagnosis of Ocular Toxoplasmosis in fundus images using convolutional neural network Choudhury, Prionto Kumar Anika, Asma Akter Ramisa, Sumaiya Rahman Zaman, Arsi Chowdhury, Rizvee Rifat Alam, Md.Golam Rabiul Reza, MD. Tanzim Department of Computer Science and Engineering, Brac University Ocular Toxoplasmosis Convolutional neural network MobileNet VGG16 ResNet50 Deep learning VGG19 Neural networks (Computer science) Eye--Diseases Diagnostic imaging Deep learning (Machine learning) This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2024. Cataloged from PDF version of thesis. Includes bibliographical references (pages 49-51). Ocular toxoplasmosis (OT) is often diagnosed by a specialist by the examination of fundus images of the eye. While deep learning is commonly used to process and identify diseases in medical images, ocular toxoplasmosis (OT) diagnosis has not received much attention up to this point.. We created and applied an effective Convolutional Neural Network (CNN) model that can accurately detect and classify Ocular Toxoplasmosis (OT) photos into four different groups: healthy, active, inactive, active-inactive. Later on, except healthy, three other classes turned to be an one class which is unhealthy. We created and applied an effective Convolutional Neural Network (CNN) model that can accurately detect and classify Ocular Toxoplasmosis (OT) photos into two different groups which are Healthy and Unhealthy. We claimed a proposed model that can accurately recognize and distinguish between the OT pictures on binary classes. In order to demonstrate the effectiveness of our customized Convolutional Neural Network (CNN) model, we employed four pre-trained models (VGG-16, VGG-19, MobileNet, ResNet50) and evaluated them using the same dataset. Our proposed custom model, along with four pretrained CNN architectures, demonstrates similar performance on the available dataset in terms of accuracy, precision, recall, and f1 score, as evaluated in this research. The proposed model shows a 95% accuracy rate. The CNN model recommended for diagnosing retinal disorders outperforms all previously utilized model. Prionto Kumar Choudhury Asma Akter Anika Sumaiya Rahman Ramisa Arsi Zaman Rizvee Rifat Chowdhury B.Sc in Computer Science and Engineering 2024-05-26T04:13:49Z 2024-05-26T04:13:49Z ©2024 2024-01 Thesis ID: 19301089 ID: 19301038 ID: 19301147 ID: 19301103 ID: 19101502 http://hdl.handle.net/10361/22916 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. 61 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Ocular Toxoplasmosis Convolutional neural network MobileNet VGG16 ResNet50 Deep learning VGG19 Neural networks (Computer science) Eye--Diseases Diagnostic imaging Deep learning (Machine learning)
spellingShingle	Ocular Toxoplasmosis Convolutional neural network MobileNet VGG16 ResNet50 Deep learning VGG19 Neural networks (Computer science) Eye--Diseases Diagnostic imaging Deep learning (Machine learning) Choudhury, Prionto Kumar Anika, Asma Akter Ramisa, Sumaiya Rahman Zaman, Arsi Chowdhury, Rizvee Rifat Deep learning based automated diagnosis of Ocular Toxoplasmosis in fundus images using convolutional neural network
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2024.
author2	Alam, Md.Golam Rabiul
author_facet	Alam, Md.Golam Rabiul Choudhury, Prionto Kumar Anika, Asma Akter Ramisa, Sumaiya Rahman Zaman, Arsi Chowdhury, Rizvee Rifat
format	Thesis
author	Choudhury, Prionto Kumar Anika, Asma Akter Ramisa, Sumaiya Rahman Zaman, Arsi Chowdhury, Rizvee Rifat
author_sort	Choudhury, Prionto Kumar
title	Deep learning based automated diagnosis of Ocular Toxoplasmosis in fundus images using convolutional neural network
title_short	Deep learning based automated diagnosis of Ocular Toxoplasmosis in fundus images using convolutional neural network
title_full	Deep learning based automated diagnosis of Ocular Toxoplasmosis in fundus images using convolutional neural network
title_fullStr	Deep learning based automated diagnosis of Ocular Toxoplasmosis in fundus images using convolutional neural network
title_full_unstemmed	Deep learning based automated diagnosis of Ocular Toxoplasmosis in fundus images using convolutional neural network
title_sort	deep learning based automated diagnosis of ocular toxoplasmosis in fundus images using convolutional neural network
publisher	Brac University
publishDate	2024
url	http://hdl.handle.net/10361/22916
work_keys_str_mv	AT choudhurypriontokumar deeplearningbasedautomateddiagnosisofoculartoxoplasmosisinfundusimagesusingconvolutionalneuralnetwork AT anikaasmaakter deeplearningbasedautomateddiagnosisofoculartoxoplasmosisinfundusimagesusingconvolutionalneuralnetwork AT ramisasumaiyarahman deeplearningbasedautomateddiagnosisofoculartoxoplasmosisinfundusimagesusingconvolutionalneuralnetwork AT zamanarsi deeplearningbasedautomateddiagnosisofoculartoxoplasmosisinfundusimagesusingconvolutionalneuralnetwork AT chowdhuryrizveerifat deeplearningbasedautomateddiagnosisofoculartoxoplasmosisinfundusimagesusingconvolutionalneuralnetwork
_version_	1814307588898029568

Deep learning based automated diagnosis of Ocular Toxoplasmosis in fundus images using convolutional neural network

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