Crossover smell agent optimized multilayer perceptron for precise brain tumor classification on MRI images
M Arumugam, A Thiyagarajan, L Adhi… - Expert Systems with …, 2024 - Elsevier
The Brain tumor is considered an unusual growth of cells in the nervous system that restricts
the normal functionality of the brain. However, is generated in the skull and pressures the …
the normal functionality of the brain. However, is generated in the skull and pressures the …
[HTML][HTML] Transfer learning architectures with fine-tuning for brain tumor classification using magnetic resonance imaging
MM Islam, P Barua, M Rahman, T Ahammed, L Akter… - Healthcare …, 2023 - Elsevier
Deep learning methods in artificial intelligence are used for brain tumor diagnosis as they
handle a huge amount of data. Compared to computerized tomography (CT), Ultrasound …
handle a huge amount of data. Compared to computerized tomography (CT), Ultrasound …
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 …
A hybrid attention-based residual Unet for semantic segmentation of brain tumor
Segmenting brain tumors in Magnetic Resonance Imaging (MRI) volumes is challenging
due to their diffuse and irregular shapes. Recently, 2D and 3D deep neural networks have …
due to their diffuse and irregular shapes. Recently, 2D and 3D deep neural networks have …
[HTML][HTML] RanMerFormer: Randomized vision transformer with token merging for brain tumor classification
Brains are the control center of the nervous system in human bodies, and brain tumor is one
of the most deadly diseases. Currently, magnetic resonance imaging (MRI) is the most …
of the most deadly diseases. Currently, magnetic resonance imaging (MRI) is the most …
Attention deep feature extraction from brain MRIs in explainable mode: Dgxainet
B Taşcı - Diagnostics, 2023 - mdpi.com
Artificial intelligence models do not provide information about exactly how the predictions
are reached. This lack of transparency is a major drawback. Particularly in medical …
are reached. This lack of transparency is a major drawback. Particularly in medical …
[HTML][HTML] An Automated Metaheuristic-optimized Approach for Diagnosing and Classifying Brain Tumors Based on a Convolutional Neural Network
Brain tumors must be classified to determine their severity and appropriate therapy. Applying
Artificial Intelligence to medical imaging has enabled remarkable developments. The …
Artificial Intelligence to medical imaging has enabled remarkable developments. The …
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 …
Monocyte/hdl cholesterol ratios as a new inflammatory marker in patients with schizophrenia
Purpose: Monocyte/HDL cholesterol ratio (MHR) is a novel inflammatory marker that is used
as a prognostic factor for cardiovascular diseases and has been studied in many diseases …
as a prognostic factor for cardiovascular diseases and has been studied in many diseases …
An integrated convolutional neural network with attention guidance for improved performance of medical image classification
Today, it becomes essential to develop computer vision algorithms that are both highly
effective and cost-effective for supporting physicians' decisions. Convolutional Neural …
effective and cost-effective for supporting physicians' decisions. Convolutional Neural …