| Autor: |
Kumar, R. Suresh, Raj, P. T. Vasanth, Praveen, K., Dhanagopal, R., Yaswanth, A. N. V., Vedavamsi, G., Chaitanya, M. |
| Zdroj: |
AIP Conference Proceedings; 2024, Vol. 2937 Issue 1, p1-7, 7p |
| Abstrakt: |
Detecting brain cancers in their earliest stages is the most important difficulty for a radiologist. The tumor doubles in size in approximately twenty-five days as it develops fast. Survival rates for those who are not adequately cared for are typically in the half-year range. This may quickly lead to death. A technology that can detect brain cancers at an early stage is preferable because of this. Magnetic resonance imaging (MRI) scans are often preferred over computed tomography (CT) scans for the detection of malignant and noncancerous lesions. Thus, a novel noise-removal approach, the modified iterative grouping median filter, is developed. Also offered for feature extraction is a kernel principal component analysis based on a maximum likelihood estimate (MLE). Segmentation was accomplished with the help of VGG16 architecture trained using deep reinforcement learning. Both qualitative and quantitative evaluations have revealed that the suggested method outperforms the well-known strategies. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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