article · Scientific Reports
Magnetic resonance imaging is the primary tool for brain tumour identification, but the complex anatomy of the brain makes accurate interpretation difficult. To tackle this, researchers evaluated deep transfer learning techniques, which repurpose existing pre-trained computational models to classify medical images even when labelled training data is limited. Four deep learning architectures, namely ResNet152, VGG19, DenseNet169, and MobileNetv3, were trained and validated using a benchmark Kaggle dataset via five-fold cross-validation. Image enhancement procedures were introduced across four specific classes: normal scans, pituitary tumours, meningioma, and glioma. Among the evaluated architectures, MobileNetv3 recorded the best performance, attaining an accuracy of 99.75 per cent. These findings indicate that transfer learning approaches, particularly models like MobileNetv3, offer high precision in distinguishing between various types of brain tumours and normal brain tissue.
Brain tumours present significant diagnostic difficulties due to the complex structure of the brain. Using pre-trained artificial intelligence models allows automated diagnosis to achieve exceptionally high accuracy without requiring enormous volumes of newly labelled clinical images. High-accuracy tools can assist medical professionals in rapidly distinguishing healthy tissue from distinct tumour types, supporting more reliable radiological assessment.
This research could enable computer-aided diagnostic software for magnetic resonance imaging, primarily for use by radiologists and diagnostic specialists. Because the findings are based entirely on benchmark Kaggle data using five-fold cross-validation, the work represents early-stage applied research. Further clinical trials and validation on diverse hospital datasets would be necessary before it could approach real-world commercialisation.
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Artificial intelligence-powered deep learning methods are being used to diagnose brain tumors with high accuracy, owing to their ability to process large amounts of data. Magnetic resonance imaging stands as the gold standard for brain tumor diagnosis using machine vision, surpassing computed tomography, ultrasound, and X-ray imaging in its effectiveness. Despite this, brain tumor diagnosis remains a challenging endeavour due to the intricate structure of the brain. This study delves into the potential of deep transfer learning architectures to elevate the accuracy of brain tumor diagnosis. Transfer learning is a machine learning technique that allows us to repurpose pre-trained models on new tasks. This can be particularly useful for medical imaging tasks, where labelled data is often scarce. Four distinct transfer learning architectures were assessed in this study: ResNet152, VGG19, DenseNet169, and MobileNetv3. The models were trained and validated on a dataset from benchmark database: Kaggle. Five-fold cross validation was adopted for training and testing. To enhance the balance of the dataset and improve the performance of the models, image enhancement techniques were applied to the data for the four categories: pituitary, normal, meningioma, and glioma. MobileNetv3 achieved the highest accuracy of 99.75%, significantly outperforming other existing methods. This demonstrates the potential of deep transfer learning architectures to revolutionize the field of brain tumor diagnosis.
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DOI: 10.1038/s41598-024-57970-7
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