Deep learning for neurodegenerative disorder (2016 to 2022): A systematic review
J Chaki, M Woźniak - Biomedical Signal Processing and Control, 2023 - Elsevier
A neurodegenerative disorder, such as Parkinson's, Alzheimer's, epilepsy, stroke, and
others, is a type of disease in which central nervous system cells stop working or die …
others, is a type of disease in which central nervous system cells stop working or die …
Diabetic retinopathy diagnosis from fundus images using stacked generalization of deep models
Diabetic retinopathy (DR) is a diabetes complication that affects the eye and can cause
damage from mild vision problems to complete blindness. It has been observed that the eye …
damage from mild vision problems to complete blindness. It has been observed that the eye …
[HTML][HTML] Accurate brain tumor detection using deep convolutional neural network
Detection and Classification of a brain tumor is an important step to better understanding its
mechanism. Magnetic Reasoning Imaging (MRI) is an experimental medical imaging …
mechanism. Magnetic Reasoning Imaging (MRI) is an experimental medical imaging …
A deep learning approach for brain tumor classification using MRI images
Brain tumors can be fatal if not detected early enough. Manually diagnosing brain tumors
requires the radiologist's experience and expertise, which may not always be available …
requires the radiologist's experience and expertise, which may not always be available …
Multi-class classification of brain tumor types from MR images using EfficientNets
Accurate classification of the type of brain tumor plays an important role in the early
diagnosis of the tumor which can be the difference between life and death. Magnetic …
diagnosis of the tumor which can be the difference between life and death. Magnetic …
Brain tumor detection based on deep learning approaches and magnetic resonance imaging
Simple Summary In this research, we addressed the challenging task of brain tumor
detection in MRI scans using a large collection of brain tumor images. We demonstrated that …
detection in MRI scans using a large collection of brain tumor images. We demonstrated that …
Intelligent ultra-light deep learning model for multi-class brain tumor detection
The diagnosis and surgical resection using Magnetic Resonance (MR) images in brain
tumors is a challenging task to minimize the neurological defects after surgery owing to the …
tumors is a challenging task to minimize the neurological defects after surgery owing to the …
Efficient 3D AlexNet architecture for object recognition using syntactic patterns from medical images
In computer vision and medical image processing, object recognition is the primary concern
today. Humans require only a few milliseconds for object recognition and visual stimulation …
today. Humans require only a few milliseconds for object recognition and visual stimulation …
[HTML][HTML] Analysis of brain MRI images using improved cornernet approach
The brain tumor is a deadly disease that is caused by the abnormal growth of brain cells,
which affects the human blood cells and nerves. Timely and precise detection of brain …
which affects the human blood cells and nerves. Timely and precise detection of brain …
[HTML][HTML] DCNet: DenseNet-77-based CornerNet model for the tomato plant leaf disease detection and classification
Early recognition of tomato plant leaf diseases is mandatory to improve the food yield and
save agriculturalists from costly spray procedures. The correct and timely identification of …
save agriculturalists from costly spray procedures. The correct and timely identification of …