Image segmentation for MR brain tumor detection using machine learning: A Review
Magnetic Resonance Imaging (MRI) has commonly been used to detect and diagnose brain
disease and monitor treatment as non-invasive imaging technology. MRI produces three …
disease and monitor treatment as non-invasive imaging technology. MRI produces three …
[HTML][HTML] Multimodal medical image fusion algorithm in the era of big data
In image-based medical decision-making, different modalities of medical images of a given
organ of a patient are captured. Each of these images will represent a modality that will …
organ of a patient are captured. Each of these images will represent a modality that will …
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 …
Multi-class brain tumor classification using residual network and global average pooling
RL Kumar, J Kakarla, BV Isunuri, M Singh - Multimedia Tools and …, 2021 - Springer
A rapid increase in brain tumor cases mandates researchers for the automation of brain
tumor detection and diagnosis. Multi-tumor brain image classification became a …
tumor detection and diagnosis. Multi-tumor brain image classification became a …
Seismic data reconstruction via wavelet-based residual deep learning
Seismic data reconstruction is one of the essential steps in the seismic data processing.
Recently, the deep learning (DL) models have attracted huge attention in seismic …
Recently, the deep learning (DL) models have attracted huge attention in seismic …
An integrated design of particle swarm optimization (PSO) with fusion of features for detection of brain tumor
Tumor in brain is a major cause of death in human beings. If not treated properly and timely,
there is a high chance of it to become malignant. Therefore, brain tumor detection at an …
there is a high chance of it to become malignant. Therefore, brain tumor detection at an …
Brain tumor detection: a long short-term memory (LSTM)-based learning model
To overcome the problems of automated brain tumor classification, a novel approach is
proposed based on long short-term memory (LSTM) model using magnetic resonance …
proposed based on long short-term memory (LSTM) model using magnetic resonance …
[HTML][HTML] A customized VGG19 network with concatenation of deep and handcrafted features for brain tumor detection
Brain tumor (BT) is one of the brain abnormalities which arises due to various reasons. The
unrecognized and untreated BT will increase the morbidity and mortality rates. The clinical …
unrecognized and untreated BT will increase the morbidity and mortality rates. The clinical …
[HTML][HTML] Brain magnetic resonance imaging classification using deep learning architectures with gender and age
Usage of effective classification techniques on Magnetic Resonance Imaging (MRI) helps in
the proper diagnosis of brain tumors. Previous studies have focused on the classification of …
the proper diagnosis of brain tumors. Previous studies have focused on the classification of …
Machine learning modeling and predictive control of the batch crystallization process
This work develops a framework for building machine learning models and machine-
learning-based predictive control schemes for batch crystallization processes. We consider …
learning-based predictive control schemes for batch crystallization processes. We consider …