A novel approach for brain tumor classification using an ensemble of deep and hand-crafted features
One of the most severe types of cancer caused by the uncontrollable proliferation of brain
cells inside the skull is brain tumors. Hence, a fast and accurate tumor detection method is …
cells inside the skull is brain tumors. Hence, a fast and accurate tumor detection method is …
Multi-class classification of brain tumour magnetic resonance images using multi-branch network with inception block and five-fold cross validation deep learning …
The expertise of radiologists plays a pivotal role in the intricate task of diagnosing brain
tumors. However, the escalating number of patients has rendered traditional diagnostic …
tumors. However, the escalating number of patients has rendered traditional diagnostic …
Enhancing brain tumor classification by a comprehensive study on transfer learning techniques and model efficiency using mri datasets
Brain tumors, a significant health concern, are a leading cause of mortality globally, with an
annual projected increase of 5% by the World Health Organization. This work aims to …
annual projected increase of 5% by the World Health Organization. This work aims to …
BrainNet: a fusion assisted novel optimal framework of residual blocks and stacked autoencoders for multimodal brain tumor classification
MS Ullah, MA Khan, NA Almujally, M Alhaisoni… - Scientific Reports, 2024 - nature.com
A significant issue in computer-aided diagnosis (CAD) for medical applications is brain
tumor classification. Radiologists could reliably detect tumors using machine learning …
tumor classification. Radiologists could reliably detect tumors using machine learning …
Brain tumor segmentation using deep learning on MRI images
AM Mostafa, M Zakariah, EA Aldakheel - Diagnostics, 2023 - mdpi.com
Brain tumor (BT) diagnosis is a lengthy process, and great skill and expertise are required
from radiologists. As the number of patients has expanded, so has the amount of data to be …
from radiologists. As the number of patients has expanded, so has the amount of data to be …
A novel lightweight CNN architecture for the diagnosis of brain tumors using MR images
Over the last few years, brain tumor-related clinical cases have increased substantially,
particularly in adults, due to environmental and genetic factors. If they are unidentified in the …
particularly in adults, due to environmental and genetic factors. If they are unidentified in the …
Brain MRI detection and classification: Harnessing convolutional neural networks and multi-level thresholding
Brain tumor detection in clinical applications is a complex and challenging task due to the
intricate structures of the human brain. Magnetic Resonance (MR) imaging is widely …
intricate structures of the human brain. Magnetic Resonance (MR) imaging is widely …
A Systematic Literature Review of CNN Approaches in Classifying Brain Tumor
AY Paulindino, B Pardamean… - 2023 6th International …, 2023 - ieeexplore.ieee.org
The ability of Convolutional Neural Networks (CNNs) to accurately discriminate between
normal and tumorous brain tissues has been promising. The review focuses on the different …
normal and tumorous brain tissues has been promising. The review focuses on the different …
Multivariate technique for the prediction and classification of brain tumor using deep shallow network
GD Krishnamoorthy, K Balasubramanian - Applied Soft Computing, 2024 - Elsevier
Brain tumors, a major cause of death, carry a substantial socio-economic burden. It is
essential to distinguish between various types of tumors, such as gliomas, meningiomas …
essential to distinguish between various types of tumors, such as gliomas, meningiomas …
Brain Tumor Classification Based Deep Transfer Learning Approaches
The group Doctors without Borders is always working to improve healthcare in
underdeveloped countries. By supporting doctors in the identification of brain tumors …
underdeveloped countries. By supporting doctors in the identification of brain tumors …