Abstract
Brain tumors are one of the most deadly disorders affecting individuals, and their early detection and accurate classification are crucial for effective treatment. Traditional biopsy methods for brain tumor grading can be invasive and pose significant risks to patients. To overcome these challenges, noninvasive imaging techniques like Magnetic Resonance Imaging (MRI) have been used. In recent years, the application of deep learning, specifically convolutional neural networks (CNNs), has shown promise in automating brain tumor detection and classification from MRI data. This paper presents a novel approach that leverages pre-trained CNN models like VGG16, ResNet50, and Inception v3 for brain tumor classification with and without transfer learning. The proposed method achieves high accuracy and precision, outperforming traditional machine learning approaches. The study also evaluates different metrics, such as sensitivity, specificity, and F1-score, to demonstrate the model’s efficacy. The results indicate that CNN-based models, especially ResNet50, offer superior performance in brain tumor classification. The proposed system has the potential to revolutionize brain tumor diagnosis and treatment planning, reducing the workload of medical professionals and improving patient outcomes. Future research may explore the integration of 3D volume-based MRI data and unsupervised transfer learning to further enhance brain tumor detection capabilities. However, it is essential to consider machine learning models as supportive tools, with final diagnosis and treatment decisions relying on the expertise of trained medical professionals. Continuous research, validation, and collaboration between ML experts and healthcare practitioners are crucial for harnessing the full potential of ML in brain tumor detection from MRI images.
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Meshram, P., Barai, T., Tahir, M., Bodhe, K. (2023). The Brain Tumors Identification, Detection, and Classification with AI/ML Algorithm with Certainty of Operations. In: Shakya, S., Tavares, J.M.R.S., Fernández-Caballero, A., Papakostas, G. (eds) Fourth International Conference on Image Processing and Capsule Networks. ICIPCN 2023. Lecture Notes in Networks and Systems, vol 798. Springer, Singapore. https://doi.org/10.1007/978-981-99-7093-3_41
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DOI: https://doi.org/10.1007/978-981-99-7093-3_41
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