| Popis: |
Introduction: The abnormal brain cells consist of brain tumor which leads to severe organ dysfunction and potentially death. These tumors exhibit a wide range of sizes, textures, and locations. Diagnosing brain tumors process is a time-consuming process requiring the expertise of radiologists. Brain tumors are classified as glioma, meningioma, pituitary, and no tumor. As patient numbers and data volumes rise, traditional methods have become costly and inefficient. Methods: Researchers have developed algorithms for detecting and classifying brain tumors and prioritizing accuracy and efficiency. Deep learning (DL) techniques are increasingly used to create automated systems capable of precisely diagnosing or segmenting brain tumors, particularly for brain cancer classification. This approach supports the use of transfer learning models in medical imaging. This proposed model is a modification to components of Xception model by adding a lot of parameters for increasing the Xception model efficiency. Results: This proposed Xception model was applied to Masoud Nickparvar braintumor- mri-dataset, achieving an accuracy of 99.6%, sensitivity of 99.7%, and specificity of 99.7% with an F1 score of 99.9%. Discussion: The efficiency parameters of the proposed model assured that it is an effective model for diagnosing brain tumor. Comparative analysis with other models shows that the proposed framework is highly reliable for the timely detection of various brain tumors. Conclusion: The results confirm the effectiveness of our proposed model, which attains higher overall accuracy in tumor detection compared to previous models. As a result, the proposed model is considered a valuable decision-making tool for experts in diagnosing brain tumor. |
