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 …
Skin cancer diagnosis based on deep transfer learning and sparrow search algorithm
HM Balaha, AES Hassan - Neural Computing and Applications, 2023 - Springer
Skin cancer affects the lives of millions of people every year, as it is considered the most
popular form of cancer. In the USA alone, approximately three and a half million people are …
popular form of cancer. In the USA alone, approximately three and a half million people are …
Brain tumor detection and segmentation in MR images using deep learning
S Sajid, S Hussain, A Sarwar - Arabian Journal for Science and …, 2019 - Springer
Gliomas are the most infiltrative and life-threatening brain tumors with exceptionally quick
development. Gliomas segmentation using computer-aided diagnosis is a challenging task …
development. Gliomas segmentation using computer-aided diagnosis is a challenging task …
Segmentation of glioma tumors in brain using deep convolutional neural network
Detection of brain tumor using a segmentation based approach is critical in cases, where
survival of a subject depends on an accurate and timely clinical diagnosis. Gliomas are the …
survival of a subject depends on an accurate and timely clinical diagnosis. Gliomas are the …
Brain structural disorders detection and classification approaches: a review
KR Bhatele, SS Bhadauria - Artificial Intelligence Review, 2020 - Springer
This paper is an effort to encapsulate the various developments in the domain of different
unsupervised, supervised and half supervised brain anomaly detection approaches or …
unsupervised, supervised and half supervised brain anomaly detection approaches or …
A multiple-kernel fuzzy c-means algorithm for image segmentation
In this paper, a generalized multiple-kernel fuzzy C-means (FCM)(MKFCM) methodology is
introduced as a framework for image-segmentation problems. In the framework, aside from …
introduced as a framework for image-segmentation problems. In the framework, aside from …
An unsupervised learning method with a clustering approach for tumor identification and tissue segmentation in magnetic resonance brain images
Malignant and benign types of tumor infiltrated in human brain are diagnosed with the help
of an MRI scanner. With the slice images obtained using an MRI scanner, certain image …
of an MRI scanner. With the slice images obtained using an MRI scanner, certain image …
A variate brain tumor segmentation, optimization, and recognition framework
HM Balaha, AES Hassan - Artificial Intelligence Review, 2023 - Springer
The detection and brain tumor (BT) segmentation and classification are mandatory steps
before any radiotherapy or surgery. When performed manually, segmentation is time …
before any radiotherapy or surgery. When performed manually, segmentation is time …
A modified possibilistic fuzzy c-means clustering algorithm for bias field estimation and segmentation of brain MR image
ZX Ji, QS Sun, DS Xia - Computerized Medical Imaging and Graphics, 2011 - Elsevier
A modified possibilistic fuzzy c-means clustering algorithm is presented for fuzzy
segmentation of magnetic resonance (MR) images that have been corrupted by intensity …
segmentation of magnetic resonance (MR) images that have been corrupted by intensity …
Accelerating compute intensive medical imaging segmentation algorithms using hybrid CPU-GPU implementations
Medical image processing is one of the most famous image processing fields in this era.
This fame comes because of the big revolution in information technology that is used to …
This fame comes because of the big revolution in information technology that is used to …