A comprehensive review on brain tumor segmentation and classification of MRI images
CS Rao, K Karunakara - Multimedia Tools and Applications, 2021 - Springer
In the analysis of medical images, one of the challenging tasks is the recognition of brain
tumours via medical resonance images (MRIs). The diagnosis process is still tedious due to …
tumours via medical resonance images (MRIs). The diagnosis process is still tedious due to …
State-of-the-art methods for brain tissue segmentation: A review
Brain tissue segmentation is one of the most sought after research areas in medical image
processing. It provides detailed quantitative brain analysis for accurate disease diagnosis …
processing. It provides detailed quantitative brain analysis for accurate disease diagnosis …
Computational framework of inverted fuzzy C-means and quantum convolutional neural network towards accurate detection of ovarian tumors
A Kodipalli, SL Fernandes, SK Dasar… - International Journal of E …, 2023 - igi-global.com
Due to the advancements in the lifestyle, stress builds enormously among individuals. A few
recent studies have indicated that stress is a major contributor for infertility and subsequent …
recent studies have indicated that stress is a major contributor for infertility and subsequent …
M3Net: A multi-model, multi-size, and multi-view deep neural network for brain magnetic resonance image segmentation
J Wei, Y ** diagnosis using valve working position and parameter optimal continuous hidden Markov model
B Zheng, X Gao - Journal of Process Control, 2017 - Elsevier
Down-hole operating condition diagnosis based on dynamometer card is a key subject for
sucker rod pum** in oil extraction engineering. In this technology, feature extraction and …
sucker rod pum** in oil extraction engineering. In this technology, feature extraction and …
Brain MRI image segmentation based on learning local variational Gaussian mixture models
Measuring the distribution of major brain tissues, including the gray matter, white matter and
cerebrospinal fluid (CSF), using magnetic resonance imaging (MRI) has attracted extensive …
cerebrospinal fluid (CSF), using magnetic resonance imaging (MRI) has attracted extensive …
Intensity inhomogeneity correction in brain MRI: a systematic review of techniques, current trends and future challenges
Intensity inhomogeneity, a common artefact in brain magnetic resonance imaging, poses
challenges in medical image analysis. Intensity inhomogeneity, also known as bias field …
challenges in medical image analysis. Intensity inhomogeneity, also known as bias field …
MRI brain segmentation in combination of clustering methods with Markov random field
Medical image segmentation is a preliminary stage of inclusion in identification tools. The
correct segmentation of brain Magnetic Resonance Imaging (MRI) images is crucial for an …
correct segmentation of brain Magnetic Resonance Imaging (MRI) images is crucial for an …
The influence of observation sequence features on the performance of the Bayesian hidden Markov model: A Monte Carlo simulation study
The hidden Markov model is a popular modeling strategy for describing and explaining
latent process dynamics. There is a lack of information on the estimation performance of the …
latent process dynamics. There is a lack of information on the estimation performance of the …