Deep learning for smart Healthcare—A survey on brain tumor detection from medical imaging

M Arabahmadi, R Farahbakhsh, J Rezazadeh - Sensors, 2022 - mdpi.com
Advances in technology have been able to affect all aspects of human life. For example, the
use of technology in medicine has made significant contributions to human society. In this …

[HTML][HTML] Applications of machine vision in pharmaceutical technology: A review

DL Galata, LA Meszaros, N Kallai-Szabo… - European Journal of …, 2021 - Elsevier
The goal of this paper is to give an introduction to analysis of images acquired by a digital
camera with visible illumination and to review its applications as a Process Analytical …

An efficient Harris hawks-inspired image segmentation method

E Rodríguez-Esparza, LA Zanella-Calzada… - Expert Systems with …, 2020 - Elsevier
Segmentation is a crucial phase in image processing because it simplifies the
representation of an image and facilitates its analysis. The multilevel thresholding method is …

Improving the segmentation of magnetic resonance brain images using the LSHADE optimization algorithm

I Aranguren, A Valdivia, B Morales-Castañeda… - … signal processing and …, 2021 - Elsevier
Segmentation is an essential preprocessing step in techniques for image analysis. The
automatic segmentation of brain magnetic resonance imaging has been exhaustively …

An efficient method for brain tumor detection and categorization using MRI images by K-means clustering & DWT

A Chaudhary, V Bhattacharjee - International Journal of Information …, 2020 - Springer
Brain tumor is an uncontrolled mass of tissues in the brain which originate due to mutated
growth of tissues. Brain tumor has become a leading cost of death in modern day …

MR images, brain lesions, and deep learning

D Castillo, V Lakshminarayanan… - Applied Sciences, 2021 - mdpi.com
Featured Application This review provides a critical review of deep/machine learning
algorithms used in the identification of ischemic stroke and demyelinating brain diseases. It …

[PDF][PDF] Automated brain tumor classification using various deep learning models: a comparative study

AA Abbood, QM Shallal… - … Journal of Electrical …, 2021 - pdfs.semanticscholar.org
The brain tumor, the most common and aggressive disease, leads to a very shorter lifespan.
Thus, planning treatments is a crucial step in improving a patient's quality of life. In general …

Brain Tumor Classification Using Conditional Segmentation with Residual Network and Attention Approach by Extreme Gradient Boost

A Hashmi, AH Osman - Applied Sciences, 2022 - mdpi.com
A brain tumor is a tumor in the brain that has grown out of control, which is a dangerous
condition for the human body. For later prognosis and treatment planning, the accurate …

An improved opposition-based Runge Kutta optimizer for multilevel image thresholding

A Casas-Ordaz, D Oliva, MA Navarro… - The Journal of …, 2023 - Springer
Minimum cross-entropy is widely used to find the best threshold values for image
segmentation; this technique is known as MCET. However, when the number of thresholds …

SVM model based computerized bone cancer detection

B Jabber, M Shankar, PV Rao… - 2020 4th International …, 2020 - ieeexplore.ieee.org
Among the many types of cancers, bone cancer is one with which most of the deaths occur
in the world. Around 10000 deaths are occurring in a year in India due to bone cancer. Bone …