kamruleee51/Skin-Lesion-Segmentation-Using-Computer-Vision-Algorithms

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kamruleee51/Skin-Lesion-Segmentation-Using-Computer-Vision-Algorithms

kamruleee51

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This project is for the skin lesion segmentation that will be used further for the classification of skin cancer.

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.

Jupyter Notebook

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.

Jupyter Notebook

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.

Jupyter Notebook

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

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.

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.

UNet-based-Lung-Segmentation

In this project, lung is segmented for the lung nodules classification. This code has been implemented in Keras API by following UNet Structure.

Jupyter Notebook

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.

Jupyter Notebook

Surgical-Tool-Type-Classification

In this project, the different types surgical tool is classified using CNN.

Jupyter Notebook

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.

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.

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.