Artificial intelligence association into brain magnetic resonance imaging (MRI) and clinical practices embracesubstantial cancer diagnosis improvement. The advancement ofdeep learning has improved the processing and analysis of MRI,boosting models' performance, decreasing the destructive effectsof data sources overload, and increasing accurate detection andtime efficacy. However, that specific dataset leads to diverseresearch fields such as image processing and analysis, detection, registration, segmentation, and classification. This paperproposes a decision-making pipeline for MRI data by combiningimage classification and segmentation. First, the pipeline shouldcorrectly produce a correct decision given an MRI image. If thefigure is classified as defective, the pipeline can extract defectregions and highlight them accordingly. We have implementedseveral advanced convolutional neural networks with transferlearning and residual techniques to address two broad clinicalconcerns in one decision-making workflow.