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Myocardial infarction detection using ECG signal applying deep learning techniques - ConvNet, VGG16, InceptionV3 and MobileNet
(Brac University, 2022-01)
Due to our unhealthy diets and the consumption of enhanced cholesterol in our
daily lives, our health has become vulnerable and at risk of different types of cardiac
diseases. The most common of them is Myocardial ...
Automatic brain tumor segmentation using U-ResUNet chain model approach
(Brac University, 2021-09)
Identifying brain tumors precisely within the early stage is still a challenging problem
for the medical sector consistent with recent research. In a previous research
approved by Cancer. Net Editorial Board, it was ...
Corn leaf disease detection using deep convolution neural network
(Brac University, 2023-01)
Detecting corn leaf diseases helps farmers identify and treat impacted crops. Early
disease identification reduces crop loss. Manual leaf diagnostic imaging takes time
and is prone to mistakes. This thesis proposes a ...
Classification of respiratory diseases and COVID-19 from respiratory and cough sound using deep learning techniques
(Brac University, 2022-01)
Infectious and non-infectious respiratory diseases are among the major reasons for
deaths, financial and social crises around the world. However, medical personnel
still find it very difficult to detect the diseases using ...
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 ...
An efficient deep learning approach for detecting Alzheimer’s disease using brain images
(Brac University, 2022-01)
Alzheimer’s disease (AD) is a disorder of the brain which causes the loss of memory.
This is a successively growing disease which means the severity of it will be upward
with the time. In this century, AD is one of the ...