sarmad-m

-Pulmonary-Fibrosis-Competition-EDA

🩺Kaggle Pulmonary Fibrosis Competition

1D-cnn-and-AdaBoost-together

alzheimers-dementia

Alzheimer's dementia classification and MMSE score regression

An-Lightweight-Multi-Scale-Feature-Fusion-Network-LMSFF-Net

APTOS2019BlindnessDetection

:3rd_place_medal: (Bronze medal - 163rd place - Top 6%) Repository for the "APTOS 2019 Blindness Detection" Kaggle competition.

Attention-Res-UNet-with-Guided-Decoder-for-Semantic-Segmentation-of-Brain-tumors

A new Deep Learning architecture for automatic segmentation of brain tumors.

AttnSleep

The code for TNSRE work: "An Attention-based Deep Learning Approach for Sleep Stage Classification with Single-Channel EEG"

audio_classification

Multi-class audio classification with MFCC features using CNN

AudioSignal_FeatureExtraction

StackCovNet

StackCovNet is an improved stacked deep convolutional network, that uses DenseNet-201, ResNet50 and VGG19 as the underlying base-learners, and a novel stacking network followed by Majority Voting in order to classify Chest X-Ray (CXR) images of COVID-19 positive patients, healthy subjects and pneumonic patients.

sarmad-m/-Pulmonary-Fibrosis-Competition-EDA

🩺Kaggle Pulmonary Fibrosis Competition

sarmad-m/alzheimers-dementia

Alzheimer's dementia classification and MMSE score regression

sarmad-m/Attention-Res-UNet-with-Guided-Decoder-for-Semantic-Segmentation-of-Brain-tumors

A new Deep Learning architecture for automatic segmentation of brain tumors.

sarmad-m/AttnSleep

The code for TNSRE work: "An Attention-based Deep Learning Approach for Sleep Stage Classification with Single-Channel EEG"

sarmad-m/Automatic_Sleep_Scoring

The goal is to create an automatic (preferably online) scoring of sleep stage scoring using raw EEG signals

sarmad-m/Characteristic-Based-Time-Series-Clustering

Time Series Clustering Based on Characteristic Based Feature Extraction inspired by the paper mentioned in the README

sarmad-m/COPDDetectionUsingAcoustics

Deep learning based respiratory sound analysis for detection of chronic obstructive pulmonary disease

sarmad-m/Deployment-of-Iris-Classifier-Machine-Learning-Model-using-Streamlit-on-Local-Webapp

This is a self-learning project. The iris classifier models is deployed on a local webapp using Streamlit. User can choose to use Linear Regression, Logistic Regression and Support Vector Machine to classify the iris.

sarmad-m/DepressionEstimation

Bachelor Thesis - Deep Learning-based Multi-modal Depression Estimation

sarmad-m/diabetic_retinopathy_detection

sarmad-m/ECG-Classfier

a ecg classfier based on PTB-XL database

sarmad-m/FQM

A web based queue management system built with Python Flask, Bootstrap and jQuery.

sarmad-m/Inception-InceptionResNet-SEInception-SEInceptionResNet-1D-2D-Tensorflow-Keras

Models Supported: Inception [v1, v2, v3, v4], SE-Inception, Inception_ResNet [v1, v2], SE-Inception_ResNet (1D and 2D version with DEMO for Classification and Regression)

sarmad-m/iris-model-deployment

ML model deployment using IRIS dataset

sarmad-m/kmeans-feature-importance

Adding feature_importances_ property to sklearn.cluster.KMeans class

sarmad-m/MixUp_augmentation

sarmad-m/MSBP_Net

MSBP_Net: Multi-scale binary pattern encoding network for cancer classification in pathology images

sarmad-m/MSRF-Net

sarmad-m/nextjs

sarmad-m/PANet

Prior Attention Network for Multi-Lesion Segmentation in Medical Images

sarmad-m/python_for_microscopists

https://www.youtube.com/channel/UC34rW-HtPJulxr5wp2Xa04w?sub_confirmation=1

sarmad-m/Safety_Helmet_mapping

sarmad-m/Sleep-Quality-Analysis

Sleep stage classification is one of the critical methodologies for the diagnosis of sleep-related diseases and complications. The conventional method of categorization is quite clumsy and timeconsuming. This project aims to devise a deep learning and machine learning model for automatic classification of sleep stage, hence, removing the barrier of conventional method and expert ubiquity. In this work, we have considered a database that carries 197-night sleep polysomnographic data. Moreover, we aimed to classify this data into stages W, N1, N2, N3 and N4 as mentioned in the AASM standard. In addition to that, we have selected the EEG FpzCz channel because of its better quality and used an epoch time of 30 seconds for signal processing. We have used four machine learning and deep learning methods, namely CNN-CNN, CNN-LSTM, Random Forest, and XGBoosting, with 82%, 87%, 51%, and 59%, respectively. This report has depicted a roadmap of the EEG-based sleep stage scoring method by implementing the state of art methods. In conclusion, using better signal processing techniques will increase the overall performance and accuracy of the model.

sarmad-m/SleepBoost

A Multi-Level Tree-based Ensemble Model for Automatic Sleep Stage Classification

sarmad-m/SleepPrintNet

SleepPrintNet: A Multivariate Multimodal Neural Network based on Physiological Time-series for Automatic Sleep Staging

sarmad-m/Spotify_Recommendation

Spotify recomendation system to help users speed up music discovery process via quickly vetting a playlist for tracks that the user would find personaly captivating. Spotify exploration to extract a users most popular tracks, binge worthy tracks, and dependable tracks.

sarmad-m/Stacked-Ensemble-of-Residual-Neural-Networks

Stacked Ensemble of three ResNetV2 models for mammograms Classification and Diagnosis

sarmad-m/tensorflow-I3D-feature-extractor

sarmad-m/test_deploy

sarmad-m/tools