Design of an Iterative Cluster-Based Model for Detection of Brain Tumors Using Deep Transfer Learning Models DOI Creative Commons

Yenumala Sankararao,

Syed Khasim

Traitement du signal, Год журнала: 2024, Номер 41(06), С. 2909 - 2922

Опубликована: Дек. 31, 2024

A tumor develops when brain cells exhibit abnormal growth patterns within various body locations, characterized by irregular boundaries and shapes.Typically, these tumors rapid proliferation, increasing at a rate of approximately 1.6% per day.This cell can lead to invisible illnesses alterations in psychological behavioral functions, contributing rising trend adult mortality rates worldwide.Therefore, Brain must be detected early.Failure do so may cause deadly, incurable condition.Effective therapy improves survival if early.Magnetic Resonance Imaging (MRI) is essential for finding classifying tumors.The manual nature diagnosis classification makes it prone errors, necessitating the development automated processes improved accuracy.In light considerations, we have devised with fully way use MR images find classify tumors.Our approach encompasses three key phases: pre-processing, segmentation, classification.To detect brain, utilized MRI, employing deep transfer transformed VGG19 model.Notably, our research demonstrates superior using other pre-trained Convolutional Neural Network (CNN) models such as AlexNet VGG-16.The learning model yielded accuracy achieving 98.65% (dataset 1) 99.18% 2) different datasets.

Язык: Английский

Advances in human activity recognition: Harnessing machine learning and deep learning with topological data analysis DOI
Hossam Magdy Balaha, Asmaa El-Sayed Hassan

Brain-Computer Interfaces, Год журнала: 2024, Номер unknown, С. 1 - 30

Опубликована: Ноя. 8, 2024

Язык: Английский

Процитировано

0
Design of an Iterative Cluster-Based Model for Detection of Brain Tumors Using Deep Transfer Learning Models DOI Creative Commons

Yenumala Sankararao,

Syed Khasim

Traitement du signal, Год журнала: 2024, Номер 41(06), С. 2909 - 2922

Опубликована: Дек. 31, 2024

A tumor develops when brain cells exhibit abnormal growth patterns within various body locations, characterized by irregular boundaries and shapes.Typically, these tumors rapid proliferation, increasing at a rate of approximately 1.6% per day.This cell can lead to invisible illnesses alterations in psychological behavioral functions, contributing rising trend adult mortality rates worldwide.Therefore, Brain must be detected early.Failure do so may cause deadly, incurable condition.Effective therapy improves survival if early.Magnetic Resonance Imaging (MRI) is essential for finding classifying tumors.The manual nature diagnosis classification makes it prone errors, necessitating the development automated processes improved accuracy.In light considerations, we have devised with fully way use MR images find classify tumors.Our approach encompasses three key phases: pre-processing, segmentation, classification.To detect brain, utilized MRI, employing deep transfer transformed VGG19 model.Notably, our research demonstrates superior using other pre-trained Convolutional Neural Network (CNN) models such as AlexNet VGG-16.The learning model yielded accuracy achieving 98.65% (dataset 1) 99.18% 2) different datasets.

Язык: Английский

Процитировано

0