Brain tumor diagnosis in MRI scans images using Residual/Shuffle Network optimized by augmented Falcon Finch optimization DOI Creative Commons

Xiaohang Guo,

Tianyi Liu, Qinglong Chi

и другие.

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Ноя. 13, 2024

Brain tumor diagnosis is an important task in prognosing and treatment planning of the patients with brain cancer. meantime, using Magnetic Resonance Imaging (MRI) as a commonly used non-invasive imaging technique provide experts helpful view for detecting tumors. While deep learning methods have shown significant success analyzing medical images, they often require careful design architecture tuning hyperparameters to achieve optimal results. This study presents new approach diagnosing tumors MRI scans learning, focusing on Residual/Shuffle Networks. The designed network structures offer efficient results when compared traditional models. To enhance proposed classification, modified metaheuristic algorithm named Augmented Falcon Finch Optimization (AFFO) introduced. AFFO utilizes bio-inspired principles effectively search best hyperparameter configurations, thereby enhancing reliability accuracy model. performance method evaluated standard dataset existing techniques, including ResNet, AlexNet, VGG-16, Inception V3, U-Net illustrate effectiveness combining Networks diagnosis.

Язык: Английский

RE-InCep-BT-:Resource-Efficient InCeptor Model for Brain Tumor Diagnostic Healthcare Applications in Computer Vision DOI

Kamini Lamba,

Shalli Rani, Muhammad Attique Khan

и другие.

Mobile Networks and Applications, Год журнала: 2024, Номер unknown

Опубликована: Апрель 15, 2024

Язык: Английский

Процитировано

4
Integrating Deep Learning and Imaging Techniques for High-Precision Brain Tumor Analysis DOI
Dilip Kumar Gokapay, Sachi Nandan Mohanty

Communications in computer and information science, Год журнала: 2025, Номер unknown, С. 53 - 67

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0
Computer-aided diagnosis for multi-class classification of brain tumors using CNN features via transfer-learning DOI
Agnesh Chandra Yadav,

Krish Shah,

Aaryan Purohit

и другие.

Multimedia Tools and Applications, Год журнала: 2025, Номер unknown

Опубликована: Март 15, 2025

Язык: Английский

Процитировано

0
An XAI-Enhanced EfficientNetB0 Framework for Precision Brain Tumor Detection in MRI Imaging DOI
T R Mahesh, Muskan Gupta, T Anupama

и другие.

Journal of Neuroscience Methods, Год журнала: 2024, Номер 410, С. 110227 - 110227

Опубликована: Июль 20, 2024

Язык: Английский

Процитировано

2
Improving Sentiment Analysis in Digital Marketplaces through SVM Kernel Fine-Tuning DOI Creative Commons
Abdul Fadlil, Imam Riadi,

Fiki Andrianto

и другие.

International Journal of Computing and Digital Systems, Год журнала: 2024, Номер 15(1), С. 159 - 171

Опубликована: Апрель 23, 2024

The rapid growth of the online market, particularly in digital realm, has spurred need for in-depth studies regarding marketing strategies through public opinion, especially on platforms like Twitter.The sentiments expressed customer tweets hold significant insights into their satisfaction or dissatisfaction levels with a service.Therefore, use ML algorithms sentiment analysis is imperative to detect whether such comments lean towards positivity negativity service.This research focuses three major e-commerce Indonesia: Tokopedia, Shopee, and Lazada, utilization classification process involves various stages, including preprocessing, feature extraction selection, data splitting classification, evaluation.The selection both linear non-linear SVM models as focus this based ability handle large complex datasets.The kernel chosen its proficiency cases relationship between features class labels, while provides flexibility dealing relationships.Based evaluation results model dataset, it found that polynomial highest accuracy value 93%, training share 85%.This strong prediction capabilities precision 93% negative positive labels.Although other kernels showed solid performance, provided most optimal context marketplace using from Twitter

Язык: Английский

Процитировано

0
Optimizing Brain Tumor Mri Classification Using Modified Vgg16 Model DOI

Ankita Mitra,

K. Sridar,

S. Rathna

и другие.

Опубликована: Авг. 23, 2024

Язык: Английский

Процитировано

0
Brain tumor diagnosis in MRI scans images using Residual/Shuffle Network optimized by augmented Falcon Finch optimization DOI Creative Commons

Xiaohang Guo,

Tianyi Liu, Qinglong Chi

и другие.

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Ноя. 13, 2024

Brain tumor diagnosis is an important task in prognosing and treatment planning of the patients with brain cancer. meantime, using Magnetic Resonance Imaging (MRI) as a commonly used non-invasive imaging technique provide experts helpful view for detecting tumors. While deep learning methods have shown significant success analyzing medical images, they often require careful design architecture tuning hyperparameters to achieve optimal results. This study presents new approach diagnosing tumors MRI scans learning, focusing on Residual/Shuffle Networks. The designed network structures offer efficient results when compared traditional models. To enhance proposed classification, modified metaheuristic algorithm named Augmented Falcon Finch Optimization (AFFO) introduced. AFFO utilizes bio-inspired principles effectively search best hyperparameter configurations, thereby enhancing reliability accuracy model. performance method evaluated standard dataset existing techniques, including ResNet, AlexNet, VGG-16, Inception V3, U-Net illustrate effectiveness combining Networks diagnosis.

Язык: Английский

Процитировано

0