An overview of artificial intelligence techniques for diagnosis of Schizophrenia based on magnetic resonance imaging modalities: Methods, challenges, and future …
Schizophrenia (SZ) is a mental disorder that typically emerges in late adolescence or early
adulthood. It reduces the life expectancy of patients by 15 years. Abnormal behavior …
adulthood. It reduces the life expectancy of patients by 15 years. Abnormal behavior …
Convolutional neural networks for multi-class brain disease detection using MRI images
The brain disorders may cause loss of some critical functions such as thinking, speech, and
movement. So, the early detection of brain diseases may help to get the timely best …
movement. So, the early detection of brain diseases may help to get the timely best …
AI‐enhanced detection of clinically relevant structural and functional anomalies in MRI: Traversing the landscape of conventional to explainable approaches
P Khosravi, S Mohammadi, F Zahiri… - Journal of Magnetic …, 2024 - Wiley Online Library
Anomaly detection in medical imaging, particularly within the realm of magnetic resonance
imaging (MRI), stands as a vital area of research with far‐reaching implications across …
imaging (MRI), stands as a vital area of research with far‐reaching implications across …
Detecting pathological brain via ResNet and randomized neural networks
Brain disease is one of the leading causes of death nowadays. Medical imaging is the most
effective method for brain disease diagnosis, which provides a clear view of the interior …
effective method for brain disease diagnosis, which provides a clear view of the interior …
Automatic brain tumor segmentation from magnetic resonance images using superpixel-based approach
Cancer is the second leading cause of deaths worldwide, reported by World Health
Organization (WHO). The abnormal growth of cells, which should die at the time but they …
Organization (WHO). The abnormal growth of cells, which should die at the time but they …
An efficient methodology for brain MRI classification based on DWT and convolutional neural network
In this paper, a model based on discrete wavelet transform and convolutional neural network
for brain MR image classification has been proposed. The proposed model is comprised of …
for brain MR image classification has been proposed. The proposed model is comprised of …
Transfer learning networks with skip connections for classification of brain tumors
This article presents a transfer learning model via convolutional neural networks (CNNs)
with skip connection topology, to avoid the vanishing gradient and time complexity, which …
with skip connection topology, to avoid the vanishing gradient and time complexity, which …
An efficient method for MRI brain tumor tissue segmentation and classification using an optimized support vector machine
S Kollem - Multimedia Tools and Applications, 2024 - Springer
Brain tumors are abnormal cell growths inside the skull that damage brain cells needed for
brain function. The complex structure of the human brain makes it challenging to identify and …
brain function. The complex structure of the human brain makes it challenging to identify and …
Classification using semantic feature and machine learning: Land-use case application
H Elmannai, AD AlGarni - … Computing Electronics and Control), 2021 - telkomnika.uad.ac.id
Land cover classification has interested recent works especially for deforestation, urban are
monitoring and agricultural land use. Traditional classification approaches have limited …
monitoring and agricultural land use. Traditional classification approaches have limited …
An automated and risk free WHO grading of glioma from MRI images using CNN
Glioma is among aggressive and common brain tumors, with a low survival rate, in its
highest grade. Invasive methods, ie, biopsy and spinal tap are clinically used to determine …
highest grade. Invasive methods, ie, biopsy and spinal tap are clinically used to determine …