Brain Tumor Detection Using a Deep CNN Model DOI Creative Commons
Sonia Ben Brahim, Samia Dardouri, Ridha Bouallègue

et al.

Applied Computational Intelligence and Soft Computing, Journal Year: 2024, Volume and Issue: 2024(1)

Published: Jan. 1, 2024

The diagnosis of brain tumors through magnetic resonance imaging (MRI) has become highly significant in the field medical science. Relying solely on MR for detection and categorization demands time, effort, expertise from professionals. This underscores need an autonomous model tumor diagnosis. Our study involves application a deep convolutional neural network (DCNN) to diagnose images. these algorithms offers several benefits, including rapid prediction, reduced errors, enhanced precision. proposed is built upon state‐of‐the‐art CNN architecture VGG16, employing data augmentation approach. dataset utilized this paper consists 3000 images sourced Kaggle, with 1500 reported contain tumors. Through training testing, pretrained achieves precision classification accuracy rate 96%, loss 1%. Moreover, it average precision, recall, F 1‐score 98.7%, 97.44%, 98.06%, respectively. These evaluation metric values demonstrate effectiveness solution.

Language: Английский

Optimizing cervical cancer classification using transfer learning with deep gaussian processes and support vector machines DOI Creative Commons
Emmanuel Ahishakiye, Fredrick Kanobe

Discover Artificial Intelligence, Journal Year: 2024, Volume and Issue: 4(1)

Published: Oct. 30, 2024

Abstract Background Cervical cancer is the fourth most frequent in women worldwide. Even though cervical deaths have decreased significantly Western countries, low and middle-income countries account for nearly 90% of deaths. While are leveraging powers artificial intelligence (AI) health sector, sub-Saharan Africa still lagging. In Uganda, cytologists manually analyze Pap smear images detection cancer, a process that highly subjective, slow, tedious. Machine learning (ML) algorithms been used automated classification cancer. However, MLs overfitting limitations which limits their deployment, especially sector where accurate predictions needed. Methods this study, we propose two kernel-based These (1) an optimized support vector machine (SVM), (2) deep Gaussian Process (DGP) model. The SVM model proposed uses radial basis kernel while DGP hybrid periodic local kernel. Results Experimental results revealed accuracy 100% 99.48% respectively. on precision, recall, F1 score were also reported. Conclusions models performed well classification, therefore suitable deployment. We plan to deploy our mobile application-based tool. limitation study was lack access high-performance computational resources.

Language: Английский

Citations

4
Advances of Artificial Intelligence in Clinical Application and Scientific Research of Neuro-oncology: Current Knowledge and Future Perspectives DOI Creative Commons
Yihong Zhan, Yuanyue Hao, Xiang Wang

et al.

Critical Reviews in Oncology/Hematology, Journal Year: 2025, Volume and Issue: unknown, P. 104682 - 104682

Published: March 1, 2025

Brain tumors refer to the abnormal growths that occur within brain's tissue, comprising both primary neoplasms and metastatic lesions. Timely detection, precise staging, suitable treatment, standardized management are of significant clinical importance for extending survival rates brain tumor patients. Artificial intelligence (AI), a discipline computer science, is leveraging its robust capacity information identification combination revolutionize traditional paradigms oncology care, offering substantial potential precision medicine. This article provides an overview current applications AI in tumors, encompassing technologies, their working mechanisms workflow, contributions diagnosis as well role scientific research, particularly drug innovation revealing microenvironment. Finally, paper addresses existing challenges, solutions, future application prospects. review aims enhance our understanding provide valuable insights forthcoming inquiries.

Language: Английский

Citations

0
X‐SCSANet: Explainable Stack Convolutional Self‐Attention Network for Brain Tumor Classification DOI Creative Commons

Rahad Khan,

Rafiqul Islam

International Journal of Intelligent Systems, Journal Year: 2025, Volume and Issue: 2025(1)

Published: Jan. 1, 2025

Brain tumors are devastating and shorten the patient’s life. It has an impact on physical, psychological, financial well‐being of both patients family members. Early diagnosis treatment can reduce patients’ chances survival. Detecting diagnosing brain cancers using MRI scans is time‐consuming requires expertise in that domain. Nowadays, instead traditional approaches to tumor analysis, several deep learning models used assist professionals mitigate time. This paper introduces a stack convolutional self‐attention network extracts important local global features from freely available scan dataset. Since medical domain one most sensitive fields, end‐users should put their trust model before automating classification. Therefore, Grad‐CAM method been updated better explain model’s output. Combining improves classification performance, with suggested reaching accuracy 96.44% relevant The proposed precision, specificity, sensitivity, F1‐score reported as 96.5%, 98.83%, 96.44%, 96.4%, respectively. Furthermore, layers’ insights examined acquire deeper knowledge decision‐making process.

Language: Английский

Citations

0
Brain Tumor Detection Using a Deep CNN Model DOI Creative Commons
Sonia Ben Brahim, Samia Dardouri, Ridha Bouallègue

et al.

Applied Computational Intelligence and Soft Computing, Journal Year: 2024, Volume and Issue: 2024(1)

Published: Jan. 1, 2024

The diagnosis of brain tumors through magnetic resonance imaging (MRI) has become highly significant in the field medical science. Relying solely on MR for detection and categorization demands time, effort, expertise from professionals. This underscores need an autonomous model tumor diagnosis. Our study involves application a deep convolutional neural network (DCNN) to diagnose images. these algorithms offers several benefits, including rapid prediction, reduced errors, enhanced precision. proposed is built upon state‐of‐the‐art CNN architecture VGG16, employing data augmentation approach. dataset utilized this paper consists 3000 images sourced Kaggle, with 1500 reported contain tumors. Through training testing, pretrained achieves precision classification accuracy rate 96%, loss 1%. Moreover, it average precision, recall, F 1‐score 98.7%, 97.44%, 98.06%, respectively. These evaluation metric values demonstrate effectiveness solution.

Language: Английский

Citations

0