Role of deep learning in brain tumor detection and classification (2015 to 2020): A review
During the last decade, computer vision and machine learning have revolutionized the world
in every way possible. Deep Learning is a sub field of machine learning that has shown …
in every way possible. Deep Learning is a sub field of machine learning that has shown …
An effective and novel approach for brain tumor classification using AlexNet CNN feature extractor and multiple eminent machine learning classifiers in MRIs
A brain tumor is an uncontrolled malignant cell growth in the brain, which is denoted as one
of the deadliest types of cancer in people of all ages. Early detection of brain tumors is …
of the deadliest types of cancer in people of all ages. Early detection of brain tumors is …
[PDF][PDF] Autism spectrum disorder classification on electroencephalogram signal using deep learning algorithm
NA Ali, AR Syafeeza, AS Jaafar… - IAES …, 2020 - download.garuda.kemdikbud.go.id
Autism Spectrum Disorder (ASD) is a neurodevelopmental that impact the social interaction
and communication skills. Diagnosis of ASD is one of the difficult problems facing …
and communication skills. Diagnosis of ASD is one of the difficult problems facing …
Deep learning-based computer-aided diagnosis (cad): applications for medical image datasets
Computer-aided diagnosis (CAD) has proved to be an effective and accurate method for
diagnostic prediction over the years. This article focuses on the development of an …
diagnostic prediction over the years. This article focuses on the development of an …
Functional magnetic resonance imaging for autism spectrum disorder detection using deep learning
Autism Spectrum Disorders (ASDs) define as a scope of disability in the development of
certain conditions such as social communication, imagination, and patients' capabilities to …
certain conditions such as social communication, imagination, and patients' capabilities to …
Brain Tumor Classification and Detection Based DL Models: A Systematic Review
In recent years, the realms of computer vision and deep learning have ushered in
transformative changes across various domains. Among these, deep learning stands out for …
transformative changes across various domains. Among these, deep learning stands out for …
Classifying the autism and epilepsy disorder based on EEG signal using deep convolutional neural network (DCNN)
In the field of Brain-Computer Interface (BCI), usage of Electroencephalogram (EEG) signals
provides various applications in many fields. EEG signals investigation is necessary due to …
provides various applications in many fields. EEG signals investigation is necessary due to …
Deep learning models for classification of brain tumor with magnetic resonance imaging images dataset
LJ Muhammad, I Badi, AA Haruna… - … Intelligence in Oncology …, 2022 - Springer
Brain tumor is known among the most aggressive diseases among adults and children
around the world. It is estimated that every year, more than 11,700 people around the world …
around the world. It is estimated that every year, more than 11,700 people around the world …
An overview of segmentation and classification techniques: A survey of brain tumour-related research
MBI Awang, S Ibrahim - 2021 2nd International Conference on …, 2021 - ieeexplore.ieee.org
A brain tumour is a condition in which brain cells develop abnormally. Early treatment can
prevent complications, which can be fatal if no early diagnosis or treatment is obtained. The …
prevent complications, which can be fatal if no early diagnosis or treatment is obtained. The …
Detection and Segmentation of Meningioma Tumors Using the Proposed MENCNN Model
JN Anita, S Kumaran - Journal of Advanced Research in …, 2023 - semarakilmu.com.my
This paper develops a Meningioma Detection and Segmentation System (MDSS) using the
proposed Meningioma Convolutional Neural Network (MENCNN) classifier. The main …
proposed Meningioma Convolutional Neural Network (MENCNN) classifier. The main …