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Cancer classification using deep learning from medical image data
(Brac University, 2022-01)
Cancer is a disease in which some of the body’s cells grow uncontrollably and spread
to other parts of the body. Cancer can start almost anywhere in the human body,
which is made up of trillions of cells. There is usually ...
Analysis of real-time hostile activitiy detection from spatiotemporal features using time distributed deep convolutional neural networks, recurrent neural networks and attention-based mechanisms
(Brac University, 2022-05)
Throughout time, there has been a surge of hostile activities in public places across
the globe. With the advancement in technology, it has been possible to monitor
public places through real time surveillance. Video ...
Epileptic seizure prediction using bandpass filtering and convolutional neural network
(Brac University, 2022-01)
Epilepsy, a chronic neurological disorder, causes seizure- a fast, uncontrollable electrical
disturbance in the brain. Seizures that last for a long time might result in
memory loss, weariness, photo sensitivity, paralysis, ...
Diabetic retinopathy detection and classification by using deep learning
(Brac University, 2022-01)
Eyes are the most sensitive part of a human being and it is one of the most challenging
tasks for a computer-aided system to classify its diseases. Many visionthreatening
diseases such as, Glaucoma and Diabetic Retinopathy ...
Intracranial hemorrhage detection using CNN-LSTM fusion model
(Brac University, 2022-05)
Intracranial Hemorrhage is a term used to describe bleeding between the brain tissue
and the skull or within the brain tissue itself. It is life-threatening and needs immediate
medical attention. As the first response, ...
An efficient approach for binary classification in brain tumor detection using convolutional neural network
(Brac University, 2022-01)
Brain tumor detection using Convolutional Neural Network (CNN) models with
binary classification has significantly improved the reliability of medical imaging
through Deep Learning. The purpose of this research is to ...
An efficient deep learning approach to detect COVID-19 infected lungs using image data
(Brac University, 2022-01)
The beginning of 2020 will always be a dreadful chapter in human history. Even
with all the recent advancements in the medical sector, the COVID-19 virus proved
to be a major challenge for doctors all over the world. The ...
An enhanced CNN model for classifying skin cancer
(Brac University, 2022-01)
Unrepaired deoxyribonucleic acid in skin cells causes skin cancer by generating genetic
abnormalities or mutations, rising day by day. Detecting and diagnosing skin
cancer in its early stages is expensive and challenging, ...
Exploring Alzheimer's disease prediction with XAI in various neural network models
(Brac University, 2021-10)
Using a number of Neural Network Models, we attempt to explore and explain the
prediction of Alzheimer's in patients in various stages of the disease, using MRI
imaging data. Alzheimer's disease(AD) often described as ...
BanglaBait: using transformers, neural networks & statistical classifiers to detect clickbaits in New Bangla Clickbait Dataset
(Brac University, 2022-01)
The art of luring us to click on certain content by exploiting our curiosity is recognized
as clickbait. Clickbait might be aggravating at times because it is misleading.
Several studies have worked on the detection of ...