
doi: 10.3233/apc210106
From each and every passing year, the world has always witnessed a rise in the number of cases of brain tumor. Brain tumor classification and detection is that the most critical and strenuous task within the field of medical image processing while human aided manual detection leads to imperfect divination and diagnosing. Brain tumors have high heterogeneity in appearance and there is a same feature between tumor and non-tumor tissues and thus the extraction of tumor regions from MRI scan images becomes unyielding. A Gray Level Co-occurrence Matrix(GLCM) is applied on MRI scan images to detect tumor and non-tumor regions in brain. The main aim of medical imaging is to extract meaningful information accurately from the images. The method of detecting brain tumor from an MRI scan images are often classified into four categories: Pre-Processing, Skull Stripping, Segmentation and have Feature Extraction.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
