Detection of multiple sclerosis using deep learning

Detection of multiple sclerosis using deep 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:	Jannat, Sabila Al, Hoque, Tanjina, Supti, Nafisa Alam
Andre forfattere:	Alam, Md. Ashraful
Format:	Thesis
Sprog:	English
Udgivet:	Brac University 2021
Fag:	Magnetic Resonance Imaging(MRI) Machine Learning Multiple Scle- rosis(MS) 3D Magnetic Resonance Imaging White Matter Lesion Detection Deep Learning Convolutional Neural Network(CNN) Fluid-attenuated inversion recovery (FLAIR) Data Augmentation Image Processing
Online adgang:	http://hdl.handle.net/10361/15737

Lignende værker

An efficient ML approach to detect brain tumor using MRI images
af: Muktadir, MD. Arafat, et al.
Udgivet: (2024)

Improvisation of Magnetic Resonance Imaging (MRI) with development and feasible evaluation of fiber optic sensors: a survey
af: Shabnam, Farzana, et al.
Udgivet: (2018)

In-depth analysis of deep learning architectures for brain tumor classification in MRI scans
af: Haque, Hossain MD. Hasibul, et al.
Udgivet: (2024)

The long road to Stockholm
af: Mansfield, P.

3D Brain image segmentation using 3D tiled convolution neural networks
af: Haque, Md Mahibul, et al.
Udgivet: (2024)

Intracranial hemorrhage detection using CNN-LSTM fusion model
af: Ahmed, Kazi sabab, et al.
Udgivet: (2022)

Enhancing multiclass brain tumor classification using deep learning: leveraging superior imaging representations to improve inferior modality performance
af: Hossain, Shah Md. Shakhawath, et al.
Udgivet: (2024)

Design and evaluation of convolutional neural network for detection of Alzheimer’s disease using MRI data
af: Mahbub, Riasat, et al.
Udgivet: (2022)

Early prediction of Alzheimer's disease using convolutional neural network
af: Abed, Mahjabeen Tamanna, et al.
Udgivet: (2020)

Classification of different magnetic structures from image data using deep neural networks
af: Ibn Hasib, Fahad, et al.
Udgivet: (2023)

Detection of acute Lymphocytic Leukemia (ALL) and its type by image processing and machine learning
af: Chowdhury, Himadri, et al.
Udgivet: (2019)

Image processing and deep learning approach to evaluate earthquake resistance of urban buildings
af: Ahmed, Yasin, et al.
Udgivet: (2018)

Image segmentation of X-Ray and optical images using U-Net/UNet++ based deep learning architecture
af: Sharma, Tanmoyee, et al.
Udgivet: (2021)

Detection of early stages of Parkinson's disease by analyzing fMRI data and machine learning approaches
af: Neehal, Ahmed Hasin, et al.
Udgivet: (2020)

Transfer learning based industrial steel plates fault diagnosis using industrial fault signals
af: Mizan, Mubasshira, et al.
Udgivet: (2024)

A novel lightweight CNN approach for Bangladeshi sign language gesture recognition
af: Rahman, Aryan, et al.
Udgivet: (2023)

Prostate cancer detection using deep learning neural network with transfer learning approach
af: Badhon, Ariful Islam Mahmud, et al.
Udgivet: (2022)

Monitoring of endangered fish using image processing and AI tools
af: Hossain, Naheen, et al.
Udgivet: (2019)

Detection of common thorax diseases from X-Ray images using a fusion of transfer and statistical learning method
af: Rafi, Ahmad Abdur, et al.
Udgivet: (2023)

Protovision: utilizing prototypical networks for retinal diseases classification based on few-shot learning
af: Nabil, Sheikh MD. Nafis Noor, et al.
Udgivet: (2024)

Child Safe Browser Extension: A Browser Extension to Detect Adultery and Violent Content to Make Safer Web for Children
af: Alhossen, Md., et al.
Udgivet: (2021)

RetinalNet-500: a newly developed CNN model for eye disease detection
af: Toki, Sadikul Alim, et al.
Udgivet: (2023)

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

Recall-Net: A CNN-based Model for Four-class Classification of Alzheimer’s Disease
af: Hasan, M. M. Kamrul, et al.
Udgivet: (2022)

Handwritten Bangla character recognition to braille pattern conversion using image processing and machine learning
af: Abir, Tawhidur Rahman, et al.
Udgivet: (2019)

ResInvolution: an involution-ResNet fused global spatial relation leveraging model for histopathological image analysis under federated learning environment
af: Dipto, Shakib Mahmud
Udgivet: (2024)

Skin cancer detection and classification using multiple optimized deep convolutional neural network
af: Sakir, Adnan, et al.
Udgivet: (2023)

Fake profile detection in social media using image processing and machine learning
af: Sen, Shuva, et al.
Udgivet: (2021)

Corn leaf disease detection using deep convolution neural network
af: Rabbi, Rawhatur, et al.
Udgivet: (2023)

Reconstruction of images with RPCA and augmenting 3D model
af: Tahiti, Tasnia Jasim, et al.
Udgivet: (2022)

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

Implementation of soft switching in resonant converters
af: Mehedy, Mehedy Hachan, et al.
Udgivet: (2018)

Detecting propagandistic poster title: a machine learning approach
af: Mahmood, Riaz, et al.
Udgivet: (2024)

Road sign detection and translation in Bangla using image processing and machine learning
af: Islam, Khalid Amirul, et al.
Udgivet: (2019)

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

Introduction to magnetism and magnetic materials /
af: Jiles, David
Udgivet: (1998)

A modern technique to detect potholes by Computer Vision and Deep Learning
af: Saif, Muntasir Mahmud, et al.
Udgivet: (2023)

Deep learning-based real-time pothole detection for avoiding road accident
af: Basher, Rafsan, et al.
Udgivet: (2022)

Classification of Bangladeshi soil texture using convolutional neural network
af: Raj, Hafiz Mohiuddin, et al.
Udgivet: (2023)

Analysis of deep learning models on low-light pest detection
af: Irtiza, Md. Samin, et al.
Udgivet: (2023)