Efficient and Accurate Brain Tumor Classification Using Hybrid MobileNetV2–Support Vector Machine for Magnetic Resonance Imaging Diagnostics in Neoplasms
Background/Objectives: Magnetic Resonance Imaging (MRI) plays a vital role in brain tumor diagnosis by providing clear visualization of soft tissues without the use of ionizing radiation. Given the increasing incidence of brain tumors, there is an urgent need for reliable diagnostic tools, as misdia...
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2024-11-01
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| author | Mohammed Jajere Adamu Halima Bello Kawuwa Li Qiang Charles Okanda Nyatega Ayesha Younis Muhammad Fahad Salisu Samaila Dauya |
| author_facet | Mohammed Jajere Adamu Halima Bello Kawuwa Li Qiang Charles Okanda Nyatega Ayesha Younis Muhammad Fahad Salisu Samaila Dauya |
| author_sort | Mohammed Jajere Adamu |
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| description | Background/Objectives: Magnetic Resonance Imaging (MRI) plays a vital role in brain tumor diagnosis by providing clear visualization of soft tissues without the use of ionizing radiation. Given the increasing incidence of brain tumors, there is an urgent need for reliable diagnostic tools, as misdiagnoses can lead to harmful treatment decisions and poor outcomes. While machine learning has significantly advanced medical diagnostics, achieving both high accuracy and computational efficiency remains a critical challenge. Methods: This study proposes a hybrid model that integrates MobileNetV2 for feature extraction with a Support Vector Machine (SVM) classifier for the classification of brain tumors. The model was trained and validated using the Kaggle MRI brain tumor dataset, which includes 7023 images categorized into four types: glioma, meningioma, pituitary tumor, and no tumor. MobileNetV2’s efficient architecture was leveraged for feature extraction, and SVM was used to enhance classification accuracy. Results: The proposed hybrid model showed excellent results, achieving Area Under the Curve (AUC) scores of 0.99 for glioma, 0.97 for meningioma, and 1.0 for both pituitary tumors and the no tumor class. These findings highlight that the MobileNetV2-SVM hybrid not only improves classification accuracy but also reduces computational overhead, making it suitable for broader clinical use. Conclusions: The MobileNetV2-SVM hybrid model demonstrates substantial potential for enhancing brain tumor diagnostics by offering a balance of precision and computational efficiency. Its ability to maintain high accuracy while operating efficiently could lead to better outcomes in medical practice, particularly in resource limited settings. |
| format | Article |
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| institution | OA Journals |
| issn | 2076-3425 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
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| series | Brain Sciences |
| spelling | doaj-art-2b8d958e550a4e5f8139dbfd4de7d8a82025-08-20T02:00:24ZengMDPI AGBrain Sciences2076-34252024-11-011412117810.3390/brainsci14121178Efficient and Accurate Brain Tumor Classification Using Hybrid MobileNetV2–Support Vector Machine for Magnetic Resonance Imaging Diagnostics in NeoplasmsMohammed Jajere Adamu0Halima Bello Kawuwa1Li Qiang2Charles Okanda Nyatega3Ayesha Younis4Muhammad Fahad5Salisu Samaila Dauya6Department of Electronic Science and Technology, School of Microelectronics, Tianjin University, Tianjin 300072, ChinaDepartment of Biomedical Engineering, School of Precision Instruments and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, ChinaDepartment of Electronic Science and Technology, School of Microelectronics, Tianjin University, Tianjin 300072, ChinaDepartment of Electronic Science and Technology, School of Microelectronics, Tianjin University, Tianjin 300072, ChinaDepartment of Electronic Science and Technology, School of Microelectronics, Tianjin University, Tianjin 300072, ChinaSchool of Electrical and Information Engineering, Tianjin University, Tianjin 300072, ChinaDepartment of Computer Science, Yobe State University, Damaturu 600213, NigeriaBackground/Objectives: Magnetic Resonance Imaging (MRI) plays a vital role in brain tumor diagnosis by providing clear visualization of soft tissues without the use of ionizing radiation. Given the increasing incidence of brain tumors, there is an urgent need for reliable diagnostic tools, as misdiagnoses can lead to harmful treatment decisions and poor outcomes. While machine learning has significantly advanced medical diagnostics, achieving both high accuracy and computational efficiency remains a critical challenge. Methods: This study proposes a hybrid model that integrates MobileNetV2 for feature extraction with a Support Vector Machine (SVM) classifier for the classification of brain tumors. The model was trained and validated using the Kaggle MRI brain tumor dataset, which includes 7023 images categorized into four types: glioma, meningioma, pituitary tumor, and no tumor. MobileNetV2’s efficient architecture was leveraged for feature extraction, and SVM was used to enhance classification accuracy. Results: The proposed hybrid model showed excellent results, achieving Area Under the Curve (AUC) scores of 0.99 for glioma, 0.97 for meningioma, and 1.0 for both pituitary tumors and the no tumor class. These findings highlight that the MobileNetV2-SVM hybrid not only improves classification accuracy but also reduces computational overhead, making it suitable for broader clinical use. Conclusions: The MobileNetV2-SVM hybrid model demonstrates substantial potential for enhancing brain tumor diagnostics by offering a balance of precision and computational efficiency. Its ability to maintain high accuracy while operating efficiently could lead to better outcomes in medical practice, particularly in resource limited settings.https://www.mdpi.com/2076-3425/14/12/1178MR imagesbrain tumorclassificationmachine and deep learningMobileNetV2SVM |
| spellingShingle | Mohammed Jajere Adamu Halima Bello Kawuwa Li Qiang Charles Okanda Nyatega Ayesha Younis Muhammad Fahad Salisu Samaila Dauya Efficient and Accurate Brain Tumor Classification Using Hybrid MobileNetV2–Support Vector Machine for Magnetic Resonance Imaging Diagnostics in Neoplasms Brain Sciences MR images brain tumor classification machine and deep learning MobileNetV2 SVM |
| title | Efficient and Accurate Brain Tumor Classification Using Hybrid MobileNetV2–Support Vector Machine for Magnetic Resonance Imaging Diagnostics in Neoplasms |
| title_full | Efficient and Accurate Brain Tumor Classification Using Hybrid MobileNetV2–Support Vector Machine for Magnetic Resonance Imaging Diagnostics in Neoplasms |
| title_fullStr | Efficient and Accurate Brain Tumor Classification Using Hybrid MobileNetV2–Support Vector Machine for Magnetic Resonance Imaging Diagnostics in Neoplasms |
| title_full_unstemmed | Efficient and Accurate Brain Tumor Classification Using Hybrid MobileNetV2–Support Vector Machine for Magnetic Resonance Imaging Diagnostics in Neoplasms |
| title_short | Efficient and Accurate Brain Tumor Classification Using Hybrid MobileNetV2–Support Vector Machine for Magnetic Resonance Imaging Diagnostics in Neoplasms |
| title_sort | efficient and accurate brain tumor classification using hybrid mobilenetv2 support vector machine for magnetic resonance imaging diagnostics in neoplasms |
| topic | MR images brain tumor classification machine and deep learning MobileNetV2 SVM |
| url | https://www.mdpi.com/2076-3425/14/12/1178 |
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