IMPROVING PREDICTION ACCURACY OF DEEP LEARNING FOR BRAIN CANCER DIAGNOSIS USING POLYAK-RUPPERT OPTIMIZATION
Accurate and reliable diagnosis is critical for effective treatment planning for brain cancer. Recent advancements in deep learning have significantly enhanced diagnostic capabilities, but challenges persist in optimizing model performance for diverse and complex datasets. This study investigates th...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
XLESCIENCE
2024-12-01
|
| Series: | International Journal of Advances in Signal and Image Sciences |
| Subjects: | |
| Online Access: | https://xlescience.org/index.php/IJASIS/article/view/173 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832583729472602112 |
|---|---|
| author | M Muthulekshmi Azath Mubarakali Y M Blessy |
| author_facet | M Muthulekshmi Azath Mubarakali Y M Blessy |
| author_sort | M Muthulekshmi |
| collection | DOAJ |
| description | Accurate and reliable diagnosis is critical for effective treatment planning for brain cancer. Recent advancements in deep learning have significantly enhanced diagnostic capabilities, but challenges persist in optimizing model performance for diverse and complex datasets. This study investigates the application of Polyak-Ruppert Optimization (PRO) to improve the prediction accuracy of conventional deep learning models for brain cancer diagnosis. Utilizing the REpository of Molecular BRAin Neoplasia DaTa (REMBRANDT) database, the proposed framework incorporates the advanced PRO technique to stabilize training and enhance generalization. The PRO’s impacts on convergence rates, model robustness, and predictive accuracy across multiple cancer types are analyzed. Experimental results demonstrate that VGG and ResNet models employing the PRO technique outperform the conventional architectures such as VGG and ResNet in classification metrics such as accuracy, sensitivity, and specificity. The potential of advanced optimization strategies such as PRO to refine deep learning applications in oncology paves the way for more accurate, efficient, and interpretable diagnostic systems. |
| format | Article |
| id | doaj-art-9356f540c4b94a3497c1f3a650320445 |
| institution | Kabale University |
| issn | 2457-0370 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | XLESCIENCE |
| record_format | Article |
| series | International Journal of Advances in Signal and Image Sciences |
| spelling | doaj-art-9356f540c4b94a3497c1f3a6503204452025-01-28T06:54:34ZengXLESCIENCEInternational Journal of Advances in Signal and Image Sciences2457-03702024-12-0110211110.29284/ijasis.10.2.2024.1-11201IMPROVING PREDICTION ACCURACY OF DEEP LEARNING FOR BRAIN CANCER DIAGNOSIS USING POLYAK-RUPPERT OPTIMIZATIONM MuthulekshmiAzath MubarakaliY M BlessyAccurate and reliable diagnosis is critical for effective treatment planning for brain cancer. Recent advancements in deep learning have significantly enhanced diagnostic capabilities, but challenges persist in optimizing model performance for diverse and complex datasets. This study investigates the application of Polyak-Ruppert Optimization (PRO) to improve the prediction accuracy of conventional deep learning models for brain cancer diagnosis. Utilizing the REpository of Molecular BRAin Neoplasia DaTa (REMBRANDT) database, the proposed framework incorporates the advanced PRO technique to stabilize training and enhance generalization. The PRO’s impacts on convergence rates, model robustness, and predictive accuracy across multiple cancer types are analyzed. Experimental results demonstrate that VGG and ResNet models employing the PRO technique outperform the conventional architectures such as VGG and ResNet in classification metrics such as accuracy, sensitivity, and specificity. The potential of advanced optimization strategies such as PRO to refine deep learning applications in oncology paves the way for more accurate, efficient, and interpretable diagnostic systems.https://xlescience.org/index.php/IJASIS/article/view/173computer-aided diagnosis, brain cancer, deep learning, convolutional neural network, polyak ruppert optimization |
| spellingShingle | M Muthulekshmi Azath Mubarakali Y M Blessy IMPROVING PREDICTION ACCURACY OF DEEP LEARNING FOR BRAIN CANCER DIAGNOSIS USING POLYAK-RUPPERT OPTIMIZATION International Journal of Advances in Signal and Image Sciences computer-aided diagnosis, brain cancer, deep learning, convolutional neural network, polyak ruppert optimization |
| title | IMPROVING PREDICTION ACCURACY OF DEEP LEARNING FOR BRAIN CANCER DIAGNOSIS USING POLYAK-RUPPERT OPTIMIZATION |
| title_full | IMPROVING PREDICTION ACCURACY OF DEEP LEARNING FOR BRAIN CANCER DIAGNOSIS USING POLYAK-RUPPERT OPTIMIZATION |
| title_fullStr | IMPROVING PREDICTION ACCURACY OF DEEP LEARNING FOR BRAIN CANCER DIAGNOSIS USING POLYAK-RUPPERT OPTIMIZATION |
| title_full_unstemmed | IMPROVING PREDICTION ACCURACY OF DEEP LEARNING FOR BRAIN CANCER DIAGNOSIS USING POLYAK-RUPPERT OPTIMIZATION |
| title_short | IMPROVING PREDICTION ACCURACY OF DEEP LEARNING FOR BRAIN CANCER DIAGNOSIS USING POLYAK-RUPPERT OPTIMIZATION |
| title_sort | improving prediction accuracy of deep learning for brain cancer diagnosis using polyak ruppert optimization |
| topic | computer-aided diagnosis, brain cancer, deep learning, convolutional neural network, polyak ruppert optimization |
| url | https://xlescience.org/index.php/IJASIS/article/view/173 |
| work_keys_str_mv | AT mmuthulekshmi improvingpredictionaccuracyofdeeplearningforbraincancerdiagnosisusingpolyakruppertoptimization AT azathmubarakali improvingpredictionaccuracyofdeeplearningforbraincancerdiagnosisusingpolyakruppertoptimization AT ymblessy improvingpredictionaccuracyofdeeplearningforbraincancerdiagnosisusingpolyakruppertoptimization |