[HTML][HTML] Brain tumor detection and classification on MR images by a deep wavelet auto-encoder model
The process of diagnosing brain tumors is very complicated for many reasons, including the
brain's synaptic structure, size, and shape. Machine learning techniques are employed to …
brain's synaptic structure, size, and shape. Machine learning techniques are employed to …
Brain MRI image classification for cancer detection using deep wavelet autoencoder-based deep neural network
Technology and the rapid growth in the area of brain imaging technologies have forever
made for a pivotal role in analyzing and focusing the new views of brain anatomy and …
made for a pivotal role in analyzing and focusing the new views of brain anatomy and …
A deep autoencoder approach for detection of brain tumor images
Brain tumor detection received much attention due to its clinical significance for early
treatment. Accurate diagnosis and classification of brain tumors are still challenging despite …
treatment. Accurate diagnosis and classification of brain tumors are still challenging despite …
An enhanced framework for identifying brain tumor using discrete wavelet transform, deep convolutional network, and feature fusion‐based machine learning …
R Mehrotra, MA Ansari, R Agrawal… - … Journal of Imaging …, 2024 - Wiley Online Library
Today, the histological study of biopsy specimens is still used to diagnose brain tumors
(BTs). This existing procedure is intrusive, arduous, and liable to mistakes. These downsides …
(BTs). This existing procedure is intrusive, arduous, and liable to mistakes. These downsides …
ICM-BTD: improved classification model for brain tumor diagnosis using discrete wavelet transform-based feature extraction and SVM classifier
A Gokulalakshmi, S Karthik, N Karthikeyan, MS Kavitha - Soft Computing, 2020 - Springer
In medical image processing, the detection, classification and segmentation of the tumor
region from MRI scans accurately are very complicated, significant and time-consuming …
region from MRI scans accurately are very complicated, significant and time-consuming …
An enhanced deep learning method for multi-class brain tumor classification using deep transfer learning
Multi-class brain tumor classification is an important area of research in the field of medical
imaging because of the different tumor characteristics. One such challenging problem is the …
imaging because of the different tumor characteristics. One such challenging problem is the …
BMRI-NET: A deep stacked ensemble model for multi-class brain tumor classification from MRI images
S Asif, M Zhao, X Chen, Y Zhu - Interdisciplinary Sciences: Computational …, 2023 - Springer
Brain tumors are one of the most dangerous health problems for adults and children in many
countries. Any failure in the diagnosis of brain tumors may lead to shortening of human life …
countries. Any failure in the diagnosis of brain tumors may lead to shortening of human life …
[HTML][HTML] Brain tumor classification in magnetic resonance images using deep learning and wavelet transform
AM Sarhan - Journal of Biomedical Science and Engineering, 2020 - scirp.org
A brain tumor is a mass of abnormal cells in the brain. Brain tumors can be benign
(noncancerous) or malignant (cancerous). Conventional diagnosis of a brain tumor by the …
(noncancerous) or malignant (cancerous). Conventional diagnosis of a brain tumor by the …
A deep transfer learning based architecture for brain tumor classification using MR images
B Badjie, ED Ülker - Information Technology and Control, 2022 - itc.ktu.lt
Deep Learning (DL) is becoming more popular in the healthcare sectors due to the
exponential growth of data availability and its excellent performance in diagnosing various …
exponential growth of data availability and its excellent performance in diagnosing various …
Deep autoencoder-based automated brain tumor detection from MRI data
F Demir - Artificial Intelligence-Based Brain-Computer Interface, 2022 - Elsevier
Brain tumor detection from magnetic resonance (MR) image samples is the core way for
radiologists, specialists, and physicians. Artificial intelligence-based MR image classification …
radiologists, specialists, and physicians. Artificial intelligence-based MR image classification …