kamruleee51

ART-Net

This project presents a Single Input Multiple Output (SIMO) deep convolutional neural network, a so-called ART-Net (Augmented Reality Tool Network) consisting of an encoder-decoder architecture to obtain the surgical tool detection, segmentation, and geometric features concurrently in an end-to-end fashion.

CVR-Net

A robust CNN-based network, called CVR-Net (Coronavirus Recognition Network), for the automatic recognition of the coronavirus from CT or X-ray images.

DdC-AC-DLIR

Deep learning image registration for cardiac motion estimation in adult and fetal echocardiography via a focus on anatomic plausibility and texture quality of warped image

Diabetes-Prediction-Using-ML-Classifiers

A robust framework was proposed where outlier rejection, filling the missing values, data standardization, K-fold validation, and different Machine Learning (ML) classifiers were used. Finally, to improve the result, weighted ensembling of different ML models also proposed.

Intensity-Based-MRI-Registration

Image registration is one of the prior steps for building computational model and Computer added diagnosis (CAD) which is the processes of transferring images into a common coordinate system, so that corresponding pixels represents homologous biological points. In this lab, we have familiarized with the concepts and framework of image registration based on two different transformation techniques namely “rigid transformation” and “affine transformation” for brain MRI. Comparisons also have been accomplished for single-resolution and multi-resolution registration for the same images in both rigid transformation and affine transformation. Different quantitative and qualitative metric performance are also been observed for all the experiments.

MRI-Brain-Segmentation-and-3D-Reconstruction-of-Brain

First of all, the brain has been segmented using image Analysis method from the dicom MRI. After that 3D reconstruction has been implemented for the 3D view of the segmented brain region.

MRI-Pre-processing

Almost in every image processing or analysis work, image pre-preprocessing is crucial step. In medical image analysis, pre-processing is a very important step because the further success or performance of the algorithm mostly dependent on pre-processed image. In this lab, we are working with 3D Brain MRI data. In case of working with brain MRI removing the noise and bias field (which is due to inhomogeneity of the magnetic field) is very important part of preprocessing of brain MRI. To do so, we widely used algorithm Anisotropic diffusion, isotropic diffusion which can diffuse in any direction, and Multiplicative intrinsic component optimization (MICO) have been used for noise removal and bias field correction respectfully. Both quantitative and qualitative performance of the algorithms also have been analyzed.

Multi-modal-MRI-Image-Segmentation-EM-algorithm-

The problem definition is to implement from scratch the algorithm of expectation maximization (EM) using Matlab. This algorithm has been applied to brain images (T1 and FLAIR). Three regions have to be segmented: the cerebrospinal fluid (CSF), the gray matter (GM), and the white matter (WM). https://ieeexplore.ieee.org/abstract/document/9420761

Recommendation-for-understanding-of-semantic-segmentation-using-CNN

Easy understanding of the semantic segmentation using CNN with some recommended links.

Skin-Lesion-Segmentation-Using-Proposed-DSNet

In this repository, the source code and segmented mask from semantic segmentation network so-called Dermoscopic Skin Network (DSNet) of the skin lesion have been added.

kamruleee51/ART-Net

This project presents a Single Input Multiple Output (SIMO) deep convolutional neural network, a so-called ART-Net (Augmented Reality Tool Network) consisting of an encoder-decoder architecture to obtain the surgical tool detection, segmentation, and geometric features concurrently in an end-to-end fashion.

kamruleee51/Skin-Lesion-Segmentation-Using-Proposed-DSNet

In this repository, the source code and segmented mask from semantic segmentation network so-called Dermoscopic Skin Network (DSNet) of the skin lesion have been added.

kamruleee51/Diabetes-Prediction-Using-ML-Classifiers

A robust framework was proposed where outlier rejection, filling the missing values, data standardization, K-fold validation, and different Machine Learning (ML) classifiers were used. Finally, to improve the result, weighted ensembling of different ML models also proposed.

kamruleee51/MRI-Pre-processing

Almost in every image processing or analysis work, image pre-preprocessing is crucial step. In medical image analysis, pre-processing is a very important step because the further success or performance of the algorithm mostly dependent on pre-processed image. In this lab, we are working with 3D Brain MRI data. In case of working with brain MRI removing the noise and bias field (which is due to inhomogeneity of the magnetic field) is very important part of preprocessing of brain MRI. To do so, we widely used algorithm Anisotropic diffusion, isotropic diffusion which can diffuse in any direction, and Multiplicative intrinsic component optimization (MICO) have been used for noise removal and bias field correction respectfully. Both quantitative and qualitative performance of the algorithms also have been analyzed.

kamruleee51/Recommendation-for-understanding-of-semantic-segmentation-using-CNN

Easy understanding of the semantic segmentation using CNN with some recommended links.

kamruleee51/Multi-modal-MRI-Image-Segmentation-EM-algorithm-

The problem definition is to implement from scratch the algorithm of expectation maximization (EM) using Matlab. This algorithm has been applied to brain images (T1 and FLAIR). Three regions have to be segmented: the cerebrospinal fluid (CSF), the gray matter (GM), and the white matter (WM). https://ieeexplore.ieee.org/abstract/document/9420761

kamruleee51/CVR-Net

A robust CNN-based network, called CVR-Net (Coronavirus Recognition Network), for the automatic recognition of the coronavirus from CT or X-ray images.

kamruleee51/Intensity-Based-MRI-Registration

Image registration is one of the prior steps for building computational model and Computer added diagnosis (CAD) which is the processes of transferring images into a common coordinate system, so that corresponding pixels represents homologous biological points. In this lab, we have familiarized with the concepts and framework of image registration based on two different transformation techniques namely “rigid transformation” and “affine transformation” for brain MRI. Comparisons also have been accomplished for single-resolution and multi-resolution registration for the same images in both rigid transformation and affine transformation. Different quantitative and qualitative metric performance are also been observed for all the experiments.

kamruleee51/DdC-AC-DLIR

Deep learning image registration for cardiac motion estimation in adult and fetal echocardiography via a focus on anatomic plausibility and texture quality of warped image

kamruleee51/Diabetes-classification-dataset

In this article, we proposed a new labeled diabetes dataset from a South Asian country (Bangladesh). Additionally, we recommended an automated classification pipeline, introducing a weighted ensemble of several Machine Learning (ML) classifiers: Naive Bayes (NB), Random Forest (RF), Decision Tree (DT), XGBoost (XGB), and LightGBM (LGB). The critical hyperparameters of these ML models are tuned using a grid search hyperparameter optimization approach. Missing values imputation, feature selection, and K-fold cross-validation were also incorporated into the designed framework.

kamruleee51/DRNet_Segmentation_Localization_OD_Fovea

We propose an end-to-end encoder-decoder network, named DRNet, for the segmentation and localization of OD and Fovea centers. In our DRNet, we propose a skip connection, named residual skip connection, for compensating the lost spatial information due to pooling in the encoder.

kamruleee51/Web-App-of-Skin-Lesion-Classification

We have implemented a web application, for skin lesion classification, by deploying the trained DermoExpert for the clinical application, which runs in a web browser.

kamruleee51/Automatic-Mass-Classification-in-Breast

kamruleee51/COVID19_imaging_AI_paper_list

COVID-19 imaging-based AI paper collection

kamruleee51/EEG-Datasets

A list of all public EEG-datasets

kamruleee51/Fashion-MNIST-Classifcations-Using-CNN

This repository is dedicated to classify images (T-shirt/top, Trouser, Pullover, Dress, Coat, Sandal, Shirt, Sneaker, Bag and Ankle boot) using CNN

kamruleee51/Feedback_DLIR

A spatial feedback attention module (FBA) to enhance unsupervised 3D DLIR

kamruleee51/forest

a PGF/TikZ-based LaTeX package for drawing (linguistic) trees

kamruleee51/git-lfs

Git extension for versioning large files

kamruleee51/Kidney-Tumor-Segmentation

kamruleee51/kits19

The official repository of the 2019 Kidney and Kidney Tumor Segmentation Challenge

kamruleee51/LNCS

Improved Lecture Notes in Computer Science (LNCS) template

kamruleee51/measles_vaccine_uptake

Using nationally representative demographic and health survey data, measles vaccine utilization has been classified, and its underlying factors are identified through an ensemble machine learning approach.

kamruleee51/MedAI

kamruleee51/Medical-image-registration

a project for developing registration tools with convolutional neural networks

kamruleee51/meep

free finite-difference time-domain (FDTD) software for electromagnetic simulations

kamruleee51/Numerical-Digit-Classifcations-Using-CNN

This repository is dedicated for handwritten digit (MNIST) recognition in Python using CNN.

kamruleee51/Projects-done-in-1st-Semester-uB-France-

Welcome to my projects page on GitHub!! All the projects that I have done are available on this page. If you need any information regarding any projects please let me know on kamruleeekuet@gmail.com OR m.k.hasan@eee.kuet.ac.bd.

kamruleee51/splncs04nat

natbib compatible splncs04.bst (Springer LNCS) BibTeX Style File built using a docstrip with the conventional merlin.mbs master file.

kamruleee51/tutorials

MONAI Tutorials