Artificial intelligence techniques for automated diagnosis of neurological disorders
Background: Authors have been advocating the research ideology that a computer-aided
diagnosis (CAD) system trained using lots of patient data and physiological signals and …
diagnosis (CAD) system trained using lots of patient data and physiological signals and …
Soft computing approaches for image segmentation: a survey
Image segmentation is the method of partitioning an image into a group of pixels that are
homogenous in some manner. The homogeneity dependents on some attributes like …
homogenous in some manner. The homogeneity dependents on some attributes like …
Image segmentation using computational intelligence techniques
Image segmentation methodology is a part of nearly all computer schemes as a pre-
processing phase to excerpt more meaningful and useful information for analysing the …
processing phase to excerpt more meaningful and useful information for analysing the …
[图书][B] Computer Vision and Recognition Systems: Research Innovations and Trends
This cutting-edge volume, Computer Vision and Recognition Systems: Research
Innovations and Trends, focuses on how artificial intelligence can be used to give computers …
Innovations and Trends, focuses on how artificial intelligence can be used to give computers …
Automated approach for detection of ischemic stroke using Delaunay Triangulation in brain MRI images
It is difficult to develop an accurate algorithm to detect the stroke lesions using magnetic
resonance imaging (MRI) images due to variation in different lesion sizes, variation in …
resonance imaging (MRI) images due to variation in different lesion sizes, variation in …
A systematic analysis of magnetic resonance images and deep learning methods used for diagnosis of brain tumor
S Solanki, UP Singh, SS Chouhan, S Jain - Multimedia Tools and …, 2024 - Springer
Accurate classification and segmentation of brain tumors is a critical task to perform. The
term classification is the process of grading tumors ie, whether the tumor is Malignant …
term classification is the process of grading tumors ie, whether the tumor is Malignant …
Generalized possibilistic fuzzy c-means with novel cluster validity indices for clustering noisy data
A generalized form of Possibilistic Fuzzy C-Means (PFCM) algorithm (GPFCM) is presented
for clustering noisy data. A function of distance is used instead of the distance itself to damp …
for clustering noisy data. A function of distance is used instead of the distance itself to damp …
Intuitionistic center-free FCM clustering for MR brain image segmentation
X Bai, Y Zhang, H Liu, Y Wang - IEEE journal of biomedical and …, 2018 - ieeexplore.ieee.org
In this paper, an intuitionistic center-free fuzzy c-means clustering method (ICFFCM) is
proposed for magnetic resonance (MR) brain image segmentation. First, in order to …
proposed for magnetic resonance (MR) brain image segmentation. First, in order to …
A gradient ascent algorithm based on possibilistic fuzzy C-Means for clustering noisy data
Real-world data are often corrupted by noise and outliers, which are originated from different
procedures such as data collection, storage, and processing. Noise and outliers decrease …
procedures such as data collection, storage, and processing. Noise and outliers decrease …
Generalized entropy based possibilistic fuzzy c-means for clustering noisy data and its convergence proof
Abstract A Generalized Entropy based Possibilistic Fuzzy C-Means algorithm (GEPFCM) is
proposed in this paper for clustering noisy data. The main objective of GEPFCM is to …
proposed in this paper for clustering noisy data. The main objective of GEPFCM is to …