A shallow convolutional neural network for cerebral neoplasm detection from magnetic resonance imaging
The effective management of cerebral carcinoma relies on early and accurate diagnosis of brain cancers. Prompt diagnosis not only helps in developing more effective treatments but also has life-saving potential. Recently, machine learning algorithms have become increasingly important in medical imag...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
REA Press
2024-06-01
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| Series: | Big Data and Computing Visions |
| Subjects: | |
| Online Access: | https://www.bidacv.com/article_205622_7a46e8ac4dddfb9f5eada85709819990.pdf |
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| Summary: | The effective management of cerebral carcinoma relies on early and accurate diagnosis of brain cancers. Prompt diagnosis not only helps in developing more effective treatments but also has life-saving potential. Recently, machine learning algorithms have become increasingly important in medical imaging and information processing, offering a robust alternative to the time-consuming and error-prone manual diagnosis of brain tumors. One prominent approach in this area is the use of Convolutional Neural Networks (CNNs), which excel in extracting significant features from medical images. These features are then used to classify Magnetic Resonance Imaging (MRI) scans, determining the presence of neural tumors. Accordingly, a shallow CNN model is proposed in this paper to classify MRI scans. The proposed model was implemented on the brain tumor MRI dataset, and the results of experiments showed promise in enhancing the accuracy of brain tumor detection, ultimately leading to better patient outcomes. The result of this study not only enables healthcare professionals to quickly and accurately identify brain malignancies but also automates the diagnostic process and minimizes dependence on manual interpretation. This approach can potentially transform cerebral carcinoma diagnosis, making it more efficient and less prone to human error. |
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| ISSN: | 2783-4956 2821-014X |