BrainTumNet: multi-task deep learning framework for brain tumor segmentation and classification using adaptive masked transformers
Background and objectiveAccurate diagnosis of brain tumors significantly impacts patient prognosis and treatment planning. Traditional diagnostic methods primarily rely on clinicians’ subjective interpretation of medical images, which is heavily dependent on physician experience and limited by time...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-05-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2025.1585891/full |
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| author | Cheng Lv Xu-Jun Shu Xu-Jun Shu Quan Liang Jun Qiu Zi-Cheng Xiong Jing bo Ye Shang bo Li Cheng Qing Liu Jing Zhen Niu Sheng-Bo Chen Hong Rao |
| author_facet | Cheng Lv Xu-Jun Shu Xu-Jun Shu Quan Liang Jun Qiu Zi-Cheng Xiong Jing bo Ye Shang bo Li Cheng Qing Liu Jing Zhen Niu Sheng-Bo Chen Hong Rao |
| author_sort | Cheng Lv |
| collection | DOAJ |
| description | Background and objectiveAccurate diagnosis of brain tumors significantly impacts patient prognosis and treatment planning. Traditional diagnostic methods primarily rely on clinicians’ subjective interpretation of medical images, which is heavily dependent on physician experience and limited by time consumption, fatigue, and inconsistent diagnoses. Recently, deep learning technologies, particularly Convolutional Neural Networks (CNN), have achieved breakthrough advances in medical image analysis, offering a new paradigm for automated precise diagnosis. However, existing research largely focuses on single-task modeling, lacking comprehensive solutions that integrate tumor segmentation with classification diagnosis. This study aims to develop a multi-task deep learning model for precise brain tumor segmentation and type classification.MethodsThe study included 485 pathologically confirmed cases, comprising T1-enhanced MRI sequence images of high-grade gliomas, metastatic tumors, and meningiomas. The dataset was proportionally divided into training (378 cases), testing (109 cases), and external validation (51 cases) sets. We designed and implemented BrainTumNet, a deep learning-based multi-task framework featuring an improved encoder-decoder architecture, adaptive masked Transformer, and multi-scale feature fusion strategy to simultaneously perform tumor region segmentation and pathological type classification. Five-fold cross-validation was employed for result verification.ResultsIn the test set evaluation, BrainTumNet achieved an Intersection over Union (IoU) of 0.921, Hausdorff Distance (HD) of 12.13, and Dice Similarity Coefficient (DSC) of 0.91 for tumor segmentation. For tumor classification, it attained a classification accuracy of 93.4% with an Area Under the ROC Curve (AUC) of 0.96. Performance remained stable on the external validation set, confirming the model’s generalization capability.ConclusionThe proposed BrainTumNet model achieves high-precision diagnosis of brain tumor segmentation and classification through a multi-task learning strategy. Experimental results demonstrate the model’s strong potential for clinical application, providing objective and reliable auxiliary information for preoperative assessment and treatment decision-making in brain tumor cases. |
| format | Article |
| id | doaj-art-a271202e1465484e8be5cdf0e9593aa0 |
| institution | OA Journals |
| issn | 2234-943X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj-art-a271202e1465484e8be5cdf0e9593aa02025-08-20T02:32:23ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2025-05-011510.3389/fonc.2025.15858911585891BrainTumNet: multi-task deep learning framework for brain tumor segmentation and classification using adaptive masked transformersCheng Lv0Xu-Jun Shu1Xu-Jun Shu2Quan Liang3Jun Qiu4Zi-Cheng Xiong5Jing bo Ye6Shang bo Li7Cheng Qing Liu8Jing Zhen Niu9Sheng-Bo Chen10Hong Rao11School of Mathematics and Computer Sciences, Nanchang University, Nanchang, Jiangxi, ChinaDepartment of Neurosurgery, General Hospital of Eastern Theater Command, Nanjing, ChinaDepartment of Neurosurgery, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, ChinaDepartment of Radiology, Jinling Hospital, Nanjing, ChinaDepartment of Critical Care Medicine, The Second People’s Hospital of Yibin, Yibin, Sichuan, ChinaDepartment of Computer and Information Engineering, Henan University, Nanchang, ChinaDepartment of Computer and Information Engineering, Henan University, Nanchang, ChinaDepartment of Computer and Information Engineering, Henan University, Nanchang, ChinaSchool of Mathematics and Computer Sciences, Nanchang University, Nanchang, Jiangxi, ChinaDepartment of Critical Care Medicine, The Second People’s Hospital of Yibin, Yibin, Sichuan, ChinaSchool of Software, Nanchang University, Nanchang, Jiangxi, ChinaSchool of Software, Nanchang University, Nanchang, Jiangxi, ChinaBackground and objectiveAccurate diagnosis of brain tumors significantly impacts patient prognosis and treatment planning. Traditional diagnostic methods primarily rely on clinicians’ subjective interpretation of medical images, which is heavily dependent on physician experience and limited by time consumption, fatigue, and inconsistent diagnoses. Recently, deep learning technologies, particularly Convolutional Neural Networks (CNN), have achieved breakthrough advances in medical image analysis, offering a new paradigm for automated precise diagnosis. However, existing research largely focuses on single-task modeling, lacking comprehensive solutions that integrate tumor segmentation with classification diagnosis. This study aims to develop a multi-task deep learning model for precise brain tumor segmentation and type classification.MethodsThe study included 485 pathologically confirmed cases, comprising T1-enhanced MRI sequence images of high-grade gliomas, metastatic tumors, and meningiomas. The dataset was proportionally divided into training (378 cases), testing (109 cases), and external validation (51 cases) sets. We designed and implemented BrainTumNet, a deep learning-based multi-task framework featuring an improved encoder-decoder architecture, adaptive masked Transformer, and multi-scale feature fusion strategy to simultaneously perform tumor region segmentation and pathological type classification. Five-fold cross-validation was employed for result verification.ResultsIn the test set evaluation, BrainTumNet achieved an Intersection over Union (IoU) of 0.921, Hausdorff Distance (HD) of 12.13, and Dice Similarity Coefficient (DSC) of 0.91 for tumor segmentation. For tumor classification, it attained a classification accuracy of 93.4% with an Area Under the ROC Curve (AUC) of 0.96. Performance remained stable on the external validation set, confirming the model’s generalization capability.ConclusionThe proposed BrainTumNet model achieves high-precision diagnosis of brain tumor segmentation and classification through a multi-task learning strategy. Experimental results demonstrate the model’s strong potential for clinical application, providing objective and reliable auxiliary information for preoperative assessment and treatment decision-making in brain tumor cases.https://www.frontiersin.org/articles/10.3389/fonc.2025.1585891/fullbrain tumor diagnosisdeep learningmulti-task learningmedical image analysisConvolutional Neural Networks |
| spellingShingle | Cheng Lv Xu-Jun Shu Xu-Jun Shu Quan Liang Jun Qiu Zi-Cheng Xiong Jing bo Ye Shang bo Li Cheng Qing Liu Jing Zhen Niu Sheng-Bo Chen Hong Rao BrainTumNet: multi-task deep learning framework for brain tumor segmentation and classification using adaptive masked transformers Frontiers in Oncology brain tumor diagnosis deep learning multi-task learning medical image analysis Convolutional Neural Networks |
| title | BrainTumNet: multi-task deep learning framework for brain tumor segmentation and classification using adaptive masked transformers |
| title_full | BrainTumNet: multi-task deep learning framework for brain tumor segmentation and classification using adaptive masked transformers |
| title_fullStr | BrainTumNet: multi-task deep learning framework for brain tumor segmentation and classification using adaptive masked transformers |
| title_full_unstemmed | BrainTumNet: multi-task deep learning framework for brain tumor segmentation and classification using adaptive masked transformers |
| title_short | BrainTumNet: multi-task deep learning framework for brain tumor segmentation and classification using adaptive masked transformers |
| title_sort | braintumnet multi task deep learning framework for brain tumor segmentation and classification using adaptive masked transformers |
| topic | brain tumor diagnosis deep learning multi-task learning medical image analysis Convolutional Neural Networks |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2025.1585891/full |
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