A Hybrid Deep Learning Model with Data Augmentation to Improve Tumor Classification Using MRI Images DOI Creative Commons
Eman M. G. Younis,

Mahmoud Nabil Mahmoud,

Abdullah M. Albarrak

et al.

Diagnostics, Journal Year: 2024, Volume and Issue: 14(23), P. 2710 - 2710

Published: Nov. 30, 2024

Cancer ranks second among the causes of mortality worldwide, following cardiovascular diseases. Brain cancer, in particular, has lowest survival rate any form cancer. tumors vary their morphology, texture, and location, which determine classification. The accurate diagnosis enables physicians to select optimal treatment strategies potentially prolong patients' lives. Researchers who have implemented deep learning models for diseases recent years largely focused on neural network optimization enhance performance. This involves implementing with best performance incorporating various architectures by configuring hyperparameters.

Language: Английский

Development of Efficient Brain Tumor Classification on MRI Image Results Using EfficientNet DOI

Faiz Ainur Razi,

Alhadi Bustamam,

Arnida L. Latifah

et al.

Published: July 26, 2023

Brain tumors are diseases that affect the most vital organs of human body. Abnormal cell development causes growth lesions in brain. In visualizing emergence a brain tumor, MRI (Magnetic Resonance Imaging) is relatively good method as it has no radiation compared to other methods. Artificial intelligence expected accelerate radiologists detecting tumor's emergence. This study proposes an automatic classification using deep learning architecture with eight EfficientNet models (BO-B7) variations classify results into normal or tumor. The perform well, which EfficientNet-B7 achieves highest training accuracy 99.71% and validation 99.67%. Compared conventional CNN, superior performance computation time. From experimental results, level CNN less than EfficienNet. indicates architectural modifications presented model, by combining layer numbers, image resolution channels can improve classifying results.

Language: Английский

Citations

5

Brain Tumor Classification using Deep Learning Framework DOI

Anuksha Srivastava,

Ashish Khare, Arati Kushwaha

et al.

Published: Feb. 3, 2023

Brain tumor classification plays a prominent rolein accurate identification of abnormal brain tissues and helps in clinical diagnosis patient. This work presents approach based on deep learning framework. Deep learning-based approaches have been used the due to its self-learning capability outperformance problems. In this work, study classification, 2D MRI data are used. The proposed method consists three stages: i) pre-processing, ii) design architecture for iii) integration conventional handcrafted features with features. A detailed has done by training architectures raw images, Local Binary Pattern (LBP) coded texture features, Discrete Wavelet Transform (DWT) coefficients classification. SoftMax classifier purpose. To authenticate method, publicly available dataset (Br35H) accuracies achieved 81.11% when is trained LBP feature, 94.11% network DWT coefficient, 94% image ResNet respectively at epochs 50. Further, effectiveness demonstrated comparing results other existing methods. experimental show efficacy over methods considered comparison.

Language: Английский

Citations

4

Brain tumor detection based on a novel and high-quality prediction of the tumor pixel distributions DOI
Yanming Sun,

Chunyan Wang

Computers in Biology and Medicine, Journal Year: 2024, Volume and Issue: 172, P. 108196 - 108196

Published: Feb. 20, 2024

Language: Английский

Citations

1

TUMbRAIN: A Transformer with a Unified Mobile Residual Attention Inverted Network for Diagnosing Brain Tumors from Magnetic Resonance Scans DOI
Francis Jesmar P. Montalbo

Neurocomputing, Journal Year: 2024, Volume and Issue: unknown, P. 128583 - 128583

Published: Sept. 1, 2024

Language: Английский

Citations

1

A Hybrid Deep Learning Model with Data Augmentation to Improve Tumor Classification Using MRI Images DOI Creative Commons
Eman M. G. Younis,

Mahmoud Nabil Mahmoud,

Abdullah M. Albarrak

et al.

Diagnostics, Journal Year: 2024, Volume and Issue: 14(23), P. 2710 - 2710

Published: Nov. 30, 2024

Cancer ranks second among the causes of mortality worldwide, following cardiovascular diseases. Brain cancer, in particular, has lowest survival rate any form cancer. tumors vary their morphology, texture, and location, which determine classification. The accurate diagnosis enables physicians to select optimal treatment strategies potentially prolong patients' lives. Researchers who have implemented deep learning models for diseases recent years largely focused on neural network optimization enhance performance. This involves implementing with best performance incorporating various architectures by configuring hyperparameters.

Language: Английский

Citations

1