Efficient and Accurate Brain Tumor Classification Using Hybrid MobileNetV2–Support Vector Machine for Magnetic Resonance Imaging Diagnostics in Neoplasms DOI Creative Commons
Mohammed Jajere Adamu, Halima Bello Kawuwa, Qiang Li

et al.

Brain Sciences, Journal Year: 2024, Volume and Issue: 14(12), P. 1178 - 1178

Published: Nov. 25, 2024

Background/Objectives: Magnetic Resonance Imaging (MRI) plays a vital role in brain tumor diagnosis by providing clear visualization of soft tissues without the use ionizing radiation. Given increasing incidence 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 computational efficiency remains critical challenge. Methods: This study proposes hybrid model that integrates MobileNetV2 feature extraction with Support Vector Machine (SVM) classifier classification tumors. The was trained validated using Kaggle MRI dataset, which includes 7023 images categorized into four types: glioma, meningioma, pituitary tumor, no tumor. MobileNetV2’s efficient architecture leveraged extraction, SVM used enhance accuracy. Results: proposed showed excellent results, Area Under Curve (AUC) scores 0.99 0.97 1.0 tumors class. These findings highlight MobileNetV2-SVM not only improves but also reduces overhead, making it suitable broader clinical use. Conclusions: demonstrates substantial potential enhancing diagnostics offering balance precision efficiency. Its ability maintain while operating efficiently could better outcomes practice, particularly resource limited settings.

Language: Английский

Machine learning fusion for glioma tumor detection DOI Creative Commons

C. Gunasundari,

K. Selva Bhuvaneswari

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 2, 2025

The early detection of brain tumors is very important for treating them and improving the quality life patients. Through advanced imaging techniques, doctors can now make more informed decisions. This paper introduces a framework tumor system capable grading gliomas. system's implementation begins with acquisition analysis magnetic resonance images. Key features indicative gliomas are extracted classified as independent components. A deep learning model then employed to categorize these proposed classifies into three primary categories: meningioma, pituitary, glioma. Performance evaluation demonstrates high level accuracy (99.21%), specificity (98.3%), sensitivity (97.83%). Further research validation essential refine ensure its clinical applicability. development accurate efficient systems holds significant promise enhancing patient care survival rates.

Language: Английский

Citations

0
A hybrid learning network with progressive resizing and PCA for diagnosis of cervical cancer on WSI slides DOI Creative Commons
Nitin Kumar Chauhan, Krishna Kant Singh, Amit Kumar

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 14, 2025

Language: Английский

Citations

0
Artificial intelligence-driven radiological biomarkers: A narrative review of artificial intelligence in meningioma diagnosis DOI Creative Commons
Antonio Navarro Ballester

NeuroMarkers., Journal Year: 2024, Volume and Issue: unknown, P. 100033 - 100033

Published: Dec. 1, 2024

Citations

1
Efficient and Accurate Brain Tumor Classification Using Hybrid MobileNetV2–Support Vector Machine for Magnetic Resonance Imaging Diagnostics in Neoplasms DOI Creative Commons
Mohammed Jajere Adamu, Halima Bello Kawuwa, Qiang Li

et al.

Brain Sciences, Journal Year: 2024, Volume and Issue: 14(12), P. 1178 - 1178

Published: Nov. 25, 2024

Background/Objectives: Magnetic Resonance Imaging (MRI) plays a vital role in brain tumor diagnosis by providing clear visualization of soft tissues without the use ionizing radiation. Given increasing incidence 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 computational efficiency remains critical challenge. Methods: This study proposes hybrid model that integrates MobileNetV2 feature extraction with Support Vector Machine (SVM) classifier classification tumors. The was trained validated using Kaggle MRI dataset, which includes 7023 images categorized into four types: glioma, meningioma, pituitary tumor, no tumor. MobileNetV2’s efficient architecture leveraged extraction, SVM used enhance accuracy. Results: proposed showed excellent results, Area Under Curve (AUC) scores 0.99 0.97 1.0 tumors class. These findings highlight MobileNetV2-SVM not only improves but also reduces overhead, making it suitable broader clinical use. Conclusions: demonstrates substantial potential enhancing diagnostics offering balance precision efficiency. Its ability maintain while operating efficiently could better outcomes practice, particularly resource limited settings.

Language: Английский

Citations

0