A review of deep learning-based multiple-lesion recognition from medical images: classification, detection and segmentation
H Jiang, Z Diao, T Shi, Y Zhou, F Wang, W Hu… - Computers in Biology …, 2023 - Elsevier
Deep learning-based methods have become the dominant methodology in medical image
processing with the advancement of deep learning in natural image classification, detection …
processing with the advancement of deep learning in natural image classification, detection …
A review of medical image data augmentation techniques for deep learning applications
Research in artificial intelligence for radiology and radiotherapy has recently become
increasingly reliant on the use of deep learning‐based algorithms. While the performance of …
increasingly reliant on the use of deep learning‐based algorithms. While the performance of …
[HTML][HTML] An efficient deep learning model to categorize brain tumor using reconstruction and fine-tuning
Brain tumors are among the most fatal and devastating diseases, often resulting in
significantly reduced life expectancy. An accurate diagnosis of brain tumors is crucial to …
significantly reduced life expectancy. An accurate diagnosis of brain tumors is crucial to …
A hybrid CNN-SVM threshold segmentation approach for tumor detection and classification of MRI brain images
Objective In this research paper, the brain MRI images are going to classify by considering
the excellence of CNN on a public dataset to classify Benign and Malignant tumors …
the excellence of CNN on a public dataset to classify Benign and Malignant tumors …
A deep learning approach for brain tumor classification and segmentation using a multiscale convolutional neural network
FJ Díaz-Pernas, M Martínez-Zarzuela… - Healthcare, 2021 - mdpi.com
In this paper, we present a fully automatic brain tumor segmentation and classification model
using a Deep Convolutional Neural Network that includes a multiscale approach. One of the …
using a Deep Convolutional Neural Network that includes a multiscale approach. One of the …
A hybrid deep learning-based approach for brain tumor classification
Brain tumors (BTs) are spreading very rapidly across the world. Every year, thousands of
people die due to deadly brain tumors. Therefore, accurate detection and classification are …
people die due to deadly brain tumors. Therefore, accurate detection and classification are …
Multi-classification of brain tumor MRI images using deep convolutional neural network with fully optimized framework
E Irmak - Iranian Journal of Science and Technology …, 2021 - Springer
Brain tumor diagnosis and classification still rely on histopathological analysis of biopsy
specimens today. The current method is invasive, time-consuming and prone to manual …
specimens today. The current method is invasive, time-consuming and prone to manual …
The state of the art in deep learning applications, challenges, and future prospects: A comprehensive review of flood forecasting and management
Floods are a devastating natural calamity that may seriously harm both infrastructure and
people. Accurate flood forecasts and control are essential to lessen these effects and …
people. Accurate flood forecasts and control are essential to lessen these effects and …
Classification of brain tumors from MRI images using a convolutional neural network
MM Badža, MČ Barjaktarović - Applied Sciences, 2020 - mdpi.com
The classification of brain tumors is performed by biopsy, which is not usually conducted
before definitive brain surgery. The improvement of technology and machine learning can …
before definitive brain surgery. The improvement of technology and machine learning can …
[HTML][HTML] Accurate brain tumor detection using deep convolutional neural network
Detection and Classification of a brain tumor is an important step to better understanding its
mechanism. Magnetic Reasoning Imaging (MRI) is an experimental medical imaging …
mechanism. Magnetic Reasoning Imaging (MRI) is an experimental medical imaging …