Automatic smart brain tumor classification and prediction system using deep learning
Abstract A brain tumor is a serious medical condition characterized by the abnormal growth of cells within the brain. It can cause a range of symptoms, including headaches, seizures, cognitive impairment, and changes in behavior. Brain tumors pose a significant health concern, imposing a substantial...
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Nature Portfolio
2025-04-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-95803-3 |
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| author | Qurat Ul Ain Ishfaq Rozi Bibi Abid Ali Faisal Jamil Yousaf Saeed Rana Othman Alnashwan Samia Allaoua Chelloug Mohammed Saleh Ali Muthanna |
| author_facet | Qurat Ul Ain Ishfaq Rozi Bibi Abid Ali Faisal Jamil Yousaf Saeed Rana Othman Alnashwan Samia Allaoua Chelloug Mohammed Saleh Ali Muthanna |
| author_sort | Qurat Ul Ain Ishfaq |
| collection | DOAJ |
| description | Abstract A brain tumor is a serious medical condition characterized by the abnormal growth of cells within the brain. It can cause a range of symptoms, including headaches, seizures, cognitive impairment, and changes in behavior. Brain tumors pose a significant health concern, imposing a substantial burden on patients. Timely diagnosis is crucial for effective treatment and patient health. Brain tumors can be either benign or malignant, and their symptoms often overlap with those of other neurological conditions, leading to delays in diagnosis. Early detection and diagnosis allow for timely intervention, potentially preventing the tumor from reaching an advanced stage. This reduces the risk of complications and increases the rate of recovery. Early detection is also significant in the selection of the most suitable treatment. In recent years, Smart IoT devices and deep learning techniques have brought remarkable success in various medical imaging applications. This study proposes a smart monitoring system for the early and timely detection, classification, and prediction of brain tumors. The proposed research employs a custom CNN model and two pre-trained models, specifically Inception-v4 and EfficientNet-B4, for classification of brain tumor cases into ten categories: Meningioma, Pituitary, No tumor, Astrocytoma, Ependymoma, Glioblastoma, Oligodendroglioma, Medulloblastoma, Germinoma, and Schwannoma. The custom CNN model is designed specifically to focus on computational efficiency and adaptability to address the unique challenges of brain tumor classification. Its adaptability to new challenges makes it a key component in the proposed smart monitoring system for brain tumor detection. Extensive experimentation is conducted to study a diverse set of brain MRI datasets and to evaluate the performance of the developed model. The model’s precision, sensitivity, accuracy, f1-score, error rate, specificity, Y-index, balanced accuracy, geometric mean, and ROC are considered as performance metrics. The average classification accuracy for CNN, Inception-v4, and EfficientNet-B4 is 97.58%, 99.56%, and 99.76%, respectively. The results demonstrate the excellent accuracy and performance of the previous proposed approaches. Furthermore, the trained models maintain accurate performance after deployment. The method predicts accuracy of 96.5% for CNN, 99.3% for Inception-v4, and 99.7% for EfficientNet-B4 on a test dataset of 1000 brain tumor images. |
| format | Article |
| id | doaj-art-1086ffa4b0734c95aba4ef564fc9656a |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-1086ffa4b0734c95aba4ef564fc9656a2025-08-20T02:03:35ZengNature PortfolioScientific Reports2045-23222025-04-0115113110.1038/s41598-025-95803-3Automatic smart brain tumor classification and prediction system using deep learningQurat Ul Ain Ishfaq0Rozi Bibi1Abid Ali2Faisal Jamil3Yousaf Saeed4Rana Othman Alnashwan5Samia Allaoua Chelloug6Mohammed Saleh Ali Muthanna7Department of Computer Science, GPGC(W)Department of Computer Science, GPGC(W)Department of Computer Science, GANK(S) DC KTSDepartment of Computer Science, School of Computing and Engineering, University of HuddersfieldDepartment of IT, The University of HaripurDepartment of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman UniversityDepartment of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman UniversityDepartment of International Business Management, Tashkent State University of EconomicsAbstract A brain tumor is a serious medical condition characterized by the abnormal growth of cells within the brain. It can cause a range of symptoms, including headaches, seizures, cognitive impairment, and changes in behavior. Brain tumors pose a significant health concern, imposing a substantial burden on patients. Timely diagnosis is crucial for effective treatment and patient health. Brain tumors can be either benign or malignant, and their symptoms often overlap with those of other neurological conditions, leading to delays in diagnosis. Early detection and diagnosis allow for timely intervention, potentially preventing the tumor from reaching an advanced stage. This reduces the risk of complications and increases the rate of recovery. Early detection is also significant in the selection of the most suitable treatment. In recent years, Smart IoT devices and deep learning techniques have brought remarkable success in various medical imaging applications. This study proposes a smart monitoring system for the early and timely detection, classification, and prediction of brain tumors. The proposed research employs a custom CNN model and two pre-trained models, specifically Inception-v4 and EfficientNet-B4, for classification of brain tumor cases into ten categories: Meningioma, Pituitary, No tumor, Astrocytoma, Ependymoma, Glioblastoma, Oligodendroglioma, Medulloblastoma, Germinoma, and Schwannoma. The custom CNN model is designed specifically to focus on computational efficiency and adaptability to address the unique challenges of brain tumor classification. Its adaptability to new challenges makes it a key component in the proposed smart monitoring system for brain tumor detection. Extensive experimentation is conducted to study a diverse set of brain MRI datasets and to evaluate the performance of the developed model. The model’s precision, sensitivity, accuracy, f1-score, error rate, specificity, Y-index, balanced accuracy, geometric mean, and ROC are considered as performance metrics. The average classification accuracy for CNN, Inception-v4, and EfficientNet-B4 is 97.58%, 99.56%, and 99.76%, respectively. The results demonstrate the excellent accuracy and performance of the previous proposed approaches. Furthermore, the trained models maintain accurate performance after deployment. The method predicts accuracy of 96.5% for CNN, 99.3% for Inception-v4, and 99.7% for EfficientNet-B4 on a test dataset of 1000 brain tumor images.https://doi.org/10.1038/s41598-025-95803-3Brain tumorDeep learningSmart healthcareCNNEfficient-b4Inception-v4 |
| spellingShingle | Qurat Ul Ain Ishfaq Rozi Bibi Abid Ali Faisal Jamil Yousaf Saeed Rana Othman Alnashwan Samia Allaoua Chelloug Mohammed Saleh Ali Muthanna Automatic smart brain tumor classification and prediction system using deep learning Scientific Reports Brain tumor Deep learning Smart healthcare CNN Efficient-b4 Inception-v4 |
| title | Automatic smart brain tumor classification and prediction system using deep learning |
| title_full | Automatic smart brain tumor classification and prediction system using deep learning |
| title_fullStr | Automatic smart brain tumor classification and prediction system using deep learning |
| title_full_unstemmed | Automatic smart brain tumor classification and prediction system using deep learning |
| title_short | Automatic smart brain tumor classification and prediction system using deep learning |
| title_sort | automatic smart brain tumor classification and prediction system using deep learning |
| topic | Brain tumor Deep learning Smart healthcare CNN Efficient-b4 Inception-v4 |
| url | https://doi.org/10.1038/s41598-025-95803-3 |
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