Automated and reliable brain radiology with texture analysis of magnetic resonance imaging and cross datasets validation

Reliable brain tumor radiology is one of the serious mortality issues of medical hospitals and on priority of healthcare departments. In this research, the presence of brain tumor and its type (if exists) is automatically diagnosed from magnetic resonance imaging (MRI). The first step is most important where suitable parameters from Gabor texture analysis are extracted and then classified with a support vector machine. The drive of this research activity is to verify robustness of the proposed model on cross datasets, so that it could deal with variability and multiformity present in MRI data. Further to this, the developed approach is able to deploy as a real application in the local environment. Therefore, once a model has been trained and tested on an openly available benchmarked dataset, it is retested on a different dataset acquired from a local source. Standard evaluation measures, that is, accuracy, specificity, sensitivity, precision, and AUC-values have been used to evaluate the robustness of the proposed method. It has been established that the proposed method has the ability to deal with multiformity, variability, and local medical traits present in brain MRI data.