Evaluation of Performance Using Cross-Validation.
<div><p>Brain tumors are one of the leading diseases imposing a huge morbidity rate across the world every year. Classifying brain tumors accurately plays a crucial role in clinical diagnosis and improves the overall healthcare process. ML techniques have shown promise in accurately clas...
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2025
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| _version_ | 1852020783815065600 |
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| author | Priyanka Roy (14580479) |
| author2 | Fahim Mohammad Sadique Srijon (19751363) Pankaj Bhowmik (6002078) |
| author2_role | author author |
| author_facet | Priyanka Roy (14580479) Fahim Mohammad Sadique Srijon (19751363) Pankaj Bhowmik (6002078) |
| author_role | author |
| dc.creator.none.fl_str_mv | Priyanka Roy (14580479) Fahim Mohammad Sadique Srijon (19751363) Pankaj Bhowmik (6002078) |
| dc.date.none.fl_str_mv | 2025-05-05T16:50:54Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0310748.t006 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Evaluation_of_Performance_Using_Cross-Validation_/28931609 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Medicine Cell Biology Genetics Cancer Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified world every year significantly improved precision significant class imbalance prioritizing stable performance planning treatment early medical image datasets leading diseases imposing handling class imbalance deep learning models brain tumor classification medical imaging data highly imbalanced data feature extraction techniques overall healthcare process overall classification process improving patient outcomes explainable ensemble approach gan mechanism facilitates classification process explainable ensemble gan mechanism techniques aid proposed mechanism ml techniques overall accuracy study proposes study presents study focuses specifically designed shown promise score demonstrate research identifies reliable model proposed pipeline original quality mri scans informative features incorporating grad frequently affected crucial role contributing parts clinical diagnosis benchmark ml based pipeline 15 %. |
| dc.title.none.fl_str_mv | Evaluation of Performance Using Cross-Validation. |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <div><p>Brain tumors are one of the leading diseases imposing a huge morbidity rate across the world every year. Classifying brain tumors accurately plays a crucial role in clinical diagnosis and improves the overall healthcare process. ML techniques have shown promise in accurately classifying brain tumors based on medical imaging data such as MRI scans. These techniques aid in detecting and planning treatment early, improving patient outcomes. However, medical image datasets are frequently affected by a significant class imbalance, especially when benign tumors outnumber malignant tumors in number. This study presents an explainable ensemble-based pipeline for brain tumor classification that integrates a Dual-GAN mechanism with feature extraction techniques, specifically designed for highly imbalanced data. This Dual-GAN mechanism facilitates the generation of synthetic minority class samples, addressing the class imbalance issue without compromising the original quality of the data. Additionally, the integration of different feature extraction methods facilitates capturing precise and informative features. This study proposes a novel deep ensemble feature extraction (DeepEFE) framework that surpasses other benchmark ML and deep learning models with an accuracy of 98.15%. This study focuses on achieving high classification accuracy while prioritizing stable performance. By incorporating Grad-CAM, it enhances the transparency and interpretability of the overall classification process. This research identifies the most relevant and contributing parts of the input images toward accurate outcomes enhancing the reliability of the proposed pipeline. The significantly improved Precision, Sensitivity and F1-Score demonstrate the effectiveness of the proposed mechanism in handling class imbalance and improving the overall accuracy. Furthermore, the integration of explainability enhances the transparency of the classification process to establish a reliable model for brain tumor classification, encouraging their adoption in clinical practice promoting trust in decision-making processes.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_008b6509ec8a67b9538871bd8a20fc49 |
| identifier_str_mv | 10.1371/journal.pone.0310748.t006 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/28931609 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Evaluation of Performance Using Cross-Validation.Priyanka Roy (14580479)Fahim Mohammad Sadique Srijon (19751363)Pankaj Bhowmik (6002078)MedicineCell BiologyGeneticsCancerSpace ScienceBiological Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedworld every yearsignificantly improved precisionsignificant class imbalanceprioritizing stable performanceplanning treatment earlymedical image datasetsleading diseases imposinghandling class imbalancedeep learning modelsbrain tumor classificationmedical imaging datahighly imbalanced datafeature extraction techniquesoverall healthcare processoverall classification processimproving patient outcomesexplainable ensemble approachgan mechanism facilitatesclassification processexplainable ensemblegan mechanismtechniques aidproposed mechanismml techniquesoverall accuracystudy proposesstudy presentsstudy focusesspecifically designedshown promisescore demonstrateresearch identifiesreliable modelproposed pipelineoriginal qualitymri scansinformative featuresincorporating gradfrequently affectedcrucial rolecontributing partsclinical diagnosisbenchmark mlbased pipeline15 %.<div><p>Brain tumors are one of the leading diseases imposing a huge morbidity rate across the world every year. Classifying brain tumors accurately plays a crucial role in clinical diagnosis and improves the overall healthcare process. ML techniques have shown promise in accurately classifying brain tumors based on medical imaging data such as MRI scans. These techniques aid in detecting and planning treatment early, improving patient outcomes. However, medical image datasets are frequently affected by a significant class imbalance, especially when benign tumors outnumber malignant tumors in number. This study presents an explainable ensemble-based pipeline for brain tumor classification that integrates a Dual-GAN mechanism with feature extraction techniques, specifically designed for highly imbalanced data. This Dual-GAN mechanism facilitates the generation of synthetic minority class samples, addressing the class imbalance issue without compromising the original quality of the data. Additionally, the integration of different feature extraction methods facilitates capturing precise and informative features. This study proposes a novel deep ensemble feature extraction (DeepEFE) framework that surpasses other benchmark ML and deep learning models with an accuracy of 98.15%. This study focuses on achieving high classification accuracy while prioritizing stable performance. By incorporating Grad-CAM, it enhances the transparency and interpretability of the overall classification process. This research identifies the most relevant and contributing parts of the input images toward accurate outcomes enhancing the reliability of the proposed pipeline. The significantly improved Precision, Sensitivity and F1-Score demonstrate the effectiveness of the proposed mechanism in handling class imbalance and improving the overall accuracy. Furthermore, the integration of explainability enhances the transparency of the classification process to establish a reliable model for brain tumor classification, encouraging their adoption in clinical practice promoting trust in decision-making processes.</p></div>2025-05-05T16:50:54ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1371/journal.pone.0310748.t006https://figshare.com/articles/dataset/Evaluation_of_Performance_Using_Cross-Validation_/28931609CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/289316092025-05-05T16:50:54Z |
| spellingShingle | Evaluation of Performance Using Cross-Validation. Priyanka Roy (14580479) Medicine Cell Biology Genetics Cancer Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified world every year significantly improved precision significant class imbalance prioritizing stable performance planning treatment early medical image datasets leading diseases imposing handling class imbalance deep learning models brain tumor classification medical imaging data highly imbalanced data feature extraction techniques overall healthcare process overall classification process improving patient outcomes explainable ensemble approach gan mechanism facilitates classification process explainable ensemble gan mechanism techniques aid proposed mechanism ml techniques overall accuracy study proposes study presents study focuses specifically designed shown promise score demonstrate research identifies reliable model proposed pipeline original quality mri scans informative features incorporating grad frequently affected crucial role contributing parts clinical diagnosis benchmark ml based pipeline 15 %. |
| status_str | publishedVersion |
| title | Evaluation of Performance Using Cross-Validation. |
| title_full | Evaluation of Performance Using Cross-Validation. |
| title_fullStr | Evaluation of Performance Using Cross-Validation. |
| title_full_unstemmed | Evaluation of Performance Using Cross-Validation. |
| title_short | Evaluation of Performance Using Cross-Validation. |
| title_sort | Evaluation of Performance Using Cross-Validation. |
| topic | Medicine Cell Biology Genetics Cancer Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified world every year significantly improved precision significant class imbalance prioritizing stable performance planning treatment early medical image datasets leading diseases imposing handling class imbalance deep learning models brain tumor classification medical imaging data highly imbalanced data feature extraction techniques overall healthcare process overall classification process improving patient outcomes explainable ensemble approach gan mechanism facilitates classification process explainable ensemble gan mechanism techniques aid proposed mechanism ml techniques overall accuracy study proposes study presents study focuses specifically designed shown promise score demonstrate research identifies reliable model proposed pipeline original quality mri scans informative features incorporating grad frequently affected crucial role contributing parts clinical diagnosis benchmark ml based pipeline 15 %. |