Search Results for author: Syed Muhammad Anwar

Found 13 papers, 1 papers with code

Mental Stress Detection using Data from Wearable and Non-wearable Sensors: A Review

no code implementations7 Feb 2022 Aamir Arsalan, Syed Muhammad Anwar, Muhammad Majid

This paper presents a comprehensive review of methods covering significant subjective and objective human stress detection techniques available in the literature.

Heart Rate Variability Mental Stress Detection

M-Net with Bidirectional ConvLSTM for Cup and Disc Segmentation in Fundus Images

no code implementations8 Apr 2021 Maleeha Khalid Khan, Syed Muhammad Anwar

Our proposed model segments cup and disc regions based on which the abnormalities in cup to disc ratio can be observed.

Deep Convolutional Neural Network based Classification of Alzheimer's Disease using MRI data

no code implementations8 Jan 2021 Ali Nawaz, Syed Muhammad Anwar, Rehan Liaqat, Javid Iqbal, Ulas Bagci, Muhammad Majid

Alzheimer's disease (AD) is a progressive and incurable neurodegenerative disease which destroys brain cells and causes loss to patient's memory.

General Classification Multi-class Classification

Variational Capsule Encoder

no code implementations18 Oct 2020 Harish RaviPrakash, Syed Muhammad Anwar, Ulas Bagci

We propose a novel capsule network based variational encoder architecture, called Bayesian capsules (B-Caps), to modulate the mean and standard deviation of the sampling distribution in the latent space.

Image Reconstruction Representation Learning

Deep Learning for Musculoskeletal Image Analysis

no code implementations1 Mar 2020 Ismail Irmakci, Syed Muhammad Anwar, Drew A. Torigian, Ulas Bagci

The diagnosis, prognosis, and treatment of patients with musculoskeletal (MSK) disorders require radiology imaging (using computed tomography, magnetic resonance imaging(MRI), and ultrasound) and their precise analysis by expert radiologists.

Classification General Classification

A Survey on Recent Advancements for AI Enabled Radiomics in Neuro-Oncology

no code implementations16 Oct 2019 Syed Muhammad Anwar, Tooba Altaf, Khola Rafique, Harish RaviPrakash, Hassan Mohy-ud-Din, Ulas Bagci

Artificial intelligence (AI) enabled radiomics has evolved immensely especially in the field of oncology.

Electroencephalography based Classification of Long-term Stress using Psychological Labeling

no code implementations17 Jul 2019 Sanay Muhammad Umar Saeed, Syed Muhammad Anwar, Humaira Khalid, Muhammad Majid, Ulas Bagci

Stress research is a rapidly emerging area in thefield of electroencephalography (EEG) based signal processing. The use of EEG as an objective measure for cost effective andpersonalized stress management becomes important in particularsituations such as the non-availability of mental health facilities. In this study, long-term stress is classified using baseline EEGsignal recordings.

Classification EEG +1

Emotion Classification in Response to Tactile Enhanced Multimedia using Frequency Domain Features of Brain Signals

no code implementations13 May 2019 Aasim Raheel, Muhammad Majid, Syed Muhammad Anwar, Ulas Bagci

The response to this enhanced multimedia content (mulsemedia) is evaluated in terms of the appreciation/emotion by using human brain signals.

EEG Emotion Classification +1

Classification of Perceived Human Stress using Physiological Signals

no code implementations13 May 2019 Aamir Arsalan, Muhammad Majid, Syed Muhammad Anwar, Ulas Bagci

In this paper, we present an experimental study for the classification of perceived human stress using non-invasive physiological signals.

Classification EEG +2

Medical Image Analysis using Convolutional Neural Networks: A Review

no code implementations4 Sep 2017 Syed Muhammad Anwar, Muhammad Majid, Adnan Qayyum, Muhammad Awais, Majdi Alnowami, Muhammad Khurram Khan

Deep learning is successfully used as a tool for machine learning, where a neural network is capable of automatically learning features.

Anomaly Detection

Segmentation of Glioma Tumors in Brain Using Deep Convolutional Neural Network

no code implementations1 Aug 2017 Saddam Hussain, Syed Muhammad Anwar, Muhammad Majid

A patch based approach along with an inception module is used for training the deep network by extracting two co-centric patches of different sizes from the input images.

Brain Tumor Segmentation Tumor Segmentation

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