Early detection of diabetic retinopathy using deep learning techniques

Early detection of diabetic retinopathy using deep learning techniques

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:	Gomes, Veronica Jessica, Alavee, Kazi Ahnaf, Sarda, Anirudh, Akhand, Zebel-E-Noor
Andre forfattere:	Mostakim, Moin
Format:	Thesis
Sprog:	English
Udgivet:	Brac University 2022
Fag:	Data preprocessing Transfer learning Convolutional neural network Xception Inception Cognitive learning theory (Deep learning) Diabetic retinopathy Electronic data processing > Data preparation 286 906984 Engineering
Online adgang:	http://hdl.handle.net/10361/15869

Lignende værker

Diabetic retinopathy detection and classification by using deep learning
af: Hossain, Shahriar, et al.
Udgivet: (2022)

Diabetic retinopathy detection using machine learning
af: Maliha, Maisha, et al.
Udgivet: (2018)

Diabetic retinopathy detection using image-processing
af: Zaman, Asif Uz, et al.
Udgivet: (2016)

A novel and superior approach to diabetic management: utilization of a fuzzy-logic based system for precision insulin dosing in type 2 diabetes patients
af: Orni, Proma Roy
Udgivet: (2018)

Detection of skin diseases using deep learning
af: Shodoy, Masuk Ur Rahman
Udgivet: (2024)

Developing a machine learning based prognostic model and a supporting web-based application for predicting the possibility of early diabetes and diabetic kidney disease
af: Amin, M.M. Shahriar, et al.
Udgivet: (2021)

An efficient deep learning approach to classify white blood cells
af: Turja, Afif Ibna Kadir Khan, et al.
Udgivet: (2024)

Hypothesizing precise cancer treatments based on patient survival using machine learning & deep learning
af: Sakib, Hasibul, et al.
Udgivet: (2024)

Visual object recognition using deep convolutional neural network
af: Ahmed, Sabbir
Udgivet: (2017)

Evaluating the effectiveness of CNN-based models for diabetic retinopathy detection
af: Niaz, H.M, et al.
Udgivet: (2024)

Detection of fake identities on twitter using supervised machine learning
af: Rahman, MD. Takiur, et al.
Udgivet: (2019)

A deep learning approach for prediction of ADHD using brain structure
af: Siam, Shafin Ahad, et al.
Udgivet: (2024)

An implementation and analysis of deep learning models for the detection of wheat diseases
af: Zubair, Ahmad, et al.
Udgivet: (2024)

Predicting depression in Bangladeshi undergraduates using machine learning
af: Choudhury, Ahnaf Atef, et al.
Udgivet: (2019)

Detection of handwritten text using convolutional neural network
af: Jasim, Rabib Bin, et al.
Udgivet: (2024)

Detection and classification of Mango Leaf diseases utilizing convolutional neural network models
af: Ferdous, Md.Rubaiat, et al.
Udgivet: (2024)

Classification of retinal diseases from OCT images using deep learning models
af: Mokarrom, Md Saif, et al.
Udgivet: (2024)

Critical retinal disease detection from optical coherence tomography images by deep convolutional neural network and explainable machine learning
af: Datta, Pranab, et al.
Udgivet: (2021)

A Study of gainful employment of learners receiving skills training in the informal sector using Machine Learning
af: Raizan, Syed Ahsan, et al.
Udgivet: (2021)

Document template identification and data extraction using machine learning and deep learning approach
af: Roy, Kaushik, et al.
Udgivet: (2024)

Classifying insect pests from image data using deep learning
af: Mohsin, Md. Raiyan Bin, et al.
Udgivet: (2022)

Pest detection system using machine learning techniques
af: Prithvi, Protyusha Barua, et al.
Udgivet: (2022)

A machine learning-based approach for data analysis to ascertain suicidal individuals from Social media users
af: Nahar, Fatiha Binte Kamrun, et al.
Udgivet: (2023)

Cassava leaf disease classification using deep learning and convolutional neural network ensemble
af: Shahriar, Hasan, et al.
Udgivet: (2022)

Impact of machine learning and deep learning on biomedical applications and healthcare industry
af: Kabir, Md Saif, et al.
Udgivet: (2024)

Comprehensive analysis and development of deep learning models for Bengali character’s spectrogram image classification in child speech: introduction of spectro SETNet
af: Ahmed, Syed Istiaque, et al.
Udgivet: (2024)

Gender classification in Bangla language using deep learning-based voice analysis
af: Hakim, Talukder Juhaer, et al.
Udgivet: (2024)

An efficient deep learning approach to detect skin Cancer
af: Islam, Ashfaqul, et al.
Udgivet: (2022)

An autoencoder-based decentralized clustering leveraging model aggregation fusion strategy
af: Hasan, Nahid
Udgivet: (2024)

An advanced data fabric architecture leveraging homomorphic encryption and federated learning
af: Rieyan, Sakib Anwar, et al.
Udgivet: (2023)

Classification of peripheral blood cell images using deep learning
af: Aadi, Oyshik Ahmed, et al.
Udgivet: (2024)

Deep convolutional GAN-based data augmentation for medical image classification
af: Datta, Joy, et al.
Udgivet: (2022)

Deep learning approaches to EEG and fMRI data: a comparative study for sleep stage classification
af: Tanvir, Farhan, et al.
Udgivet: (2024)

Medical image reader powered by artificial intelligence
af: Palok, Tanvir Ahmed, et al.
Udgivet: (2024)

Generation of realistic images from human drawn sketches using deep learning
af: Mahbub, Mohammed Julfikar Ali, et al.
Udgivet: (2022)

A novel approach to forecast traffic congestion using CMTF and machine learning
af: Chowdhury, Md. Mohiuddin, et al.
Udgivet: (2018)

Start up business solution using machine learning
af: Sohan, Saad Ahmed, et al.
Udgivet: (2018)

Machine learning and statistics : the interface /
Udgivet: (1997)

Verifying online signatures through an iterative device independent model
af: Tahsin, Samiha, et al.
Udgivet: (2024)

A secured federated learning system leveraging confidence score to identify retinal disease
af: Eshan, M Sakib Osman, et al.
Udgivet: (2023)