Md. Kamrul Hasan's repositories
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.
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.
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.
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.
Recommendation-for-understanding-of-semantic-segmentation-using-CNN
Easy understanding of the semantic segmentation using CNN with some recommended links.
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
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.
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.
DdC-AC-DLIR
Multi-scale, Data-driven and Anatomically Constrained Deep Learning Image Registration for Adult and Fetal Echocardiography
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.
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.
COVID19_imaging_AI_paper_list
COVID-19 imaging-based AI paper collection
EEG-Datasets
A list of all public EEG-datasets
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
forest
a PGF/TikZ-based LaTeX package for drawing (linguistic) trees
git-lfs
Git extension for versioning large files
LNCS
Improved Lecture Notes in Computer Science (LNCS) template
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.
Medical-image-registration
a project for developing registration tools with convolutional neural networks
meep
free finite-difference time-domain (FDTD) software for electromagnetic simulations
Numerical-Digit-Classifcations-Using-CNN
This repository is dedicated for handwritten digit (MNIST) recognition in Python using CNN.
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.
splncs04nat
natbib compatible splncs04.bst (Springer LNCS) BibTeX Style File built using a docstrip with the conventional merlin.mbs master file.
tutorials
MONAI Tutorials