Meningioma brain tumor detection and classification using hybrid CNN method and RIDGELET transform

BV Prakash, AR Kannan, N Santhiyakumari… - Scientific Reports, 2023 - nature.com
The detection of meningioma tumors is the most crucial task compared with other tumors
because of their lower pixel intensity. Modern medical platforms require a fully automated …

Brain tumor detection and categorization with segmentation of improved unsupervised clustering approach and machine learning classifier

U Bhimavarapu, N Chintalapudi, G Battineni - Bioengineering, 2024 - mdpi.com
There is no doubt that brain tumors are one of the leading causes of death in the world. A
biopsy is considered the most important procedure in cancer diagnosis, but it comes with …

Computer-aided detection and classification of brain tumor using YOLOv3 and deep learning

MM Chanu, NH Singh, C Muppala, RT Prabu… - Soft Computing, 2023 - Springer
The human brain has a complicated structure, making it difficult to diagnose any brain
ailments, particularly when they affect important parts of the brain. A brain tumor with a high …

Multi‐classification of brain tumor by using deep convolutional neural network model in magnetic resonance imaging images

NH Singh, NRG Merlin, RT Prabu… - … Journal of Imaging …, 2024 - Wiley Online Library
Brain tumors are still diagnosed and classified based on the results of histopathological
examinations of biopsy samples. The existing method requires extra effort from the user …

Multi-objective optimization of MQL system parameters for the roller burnishing operation for energy saving, product quality and air pollution

AL Van, TT Nguyen, XB Dang, PN Huu - Soft Computing, 2024 - Springer
Internal burnishing operation is a prominent solution to improve the hole quality. In this
study, minimum quantity lubrication (MQL) system parameters, including the diameter of the …

Ultrasound image segmentation using Gamma combined with Bayesian model for focused-ultrasound-surgery lesion recognition

Q Zhang, X Liu, J Chang, M Lu, Y Jing, R Yang, W Sun… - Ultrasonics, 2023 - Elsevier
This study aims to investigate the feasibility of combined segmentation for the separation of
lesions from non-ablated regions, which allows surgeons to easily distinguish, measure, and …

Hybrid Intelligent Pattern Recognition Systems for Mass Segmentation and Classification: A Pilot Study on Full-Field Digital Mammograms

A Dounis, AN Avramopoulos, M Kallergi - Applied Sciences, 2023 - mdpi.com
Governments and health authorities emphasize the importance of early detection of breast
cancer, usually through mammography, to improve prognosis, increase therapeutic options …

A Multiscale Atrous Convolution-based Adaptive ResUNet3+ with Attention-based ensemble convolution networks for brain tumour segmentation and classification …

BS Reddy, A Sathish - Biomedical Signal Processing and Control, 2024 - Elsevier
A brain tumor is a tissue group formed by the addition of unusual cells in the brain, and it's
significant to identify brain tumor through Magnetic Resonance Imaging (MRI) for treatment …

Comparison of ANN and ANFIS Models for AF Diagnosis Using RR Irregularities

S Duangburong, B Phruksaphanrat… - Applied Sciences, 2023 - mdpi.com
Classification of normal sinus rhythm (NSR), paroxysmal atrial fibrillation (PAF), and
persistent atrial fibrillation (AF) is crucial in order to diagnose and effectively plan treatment …

[PDF][PDF] Brain Tumor Segmentation and Classification Using Binomial Thresholding-Based Bidirectional-Long-Short Term Memory.

J Shreeharsha - International Journal of Intelligent Engineering & …, 2024 - inass.org
A brain tumor arises when abnormal cells develop in the brain, leading to an elevated risk of
illness and mortality due to the accelerated growth of these tumor cells. Magnetic resonance …