Classification of MRI images of brain cancer using deep learning methods such as CNN's is an increasingly popular method to detect cancer and its spread. In the present work, we perform a competitive analysis of hybrid quantum CNN based methods to classify the MRI images of brain cancer into three different classes using quantum simulators. The quantum image processing is done via three encoding schemes, viz. QCNN, FRQI and NEQR. We see that QCNN has higher accuracy of 87% with a precision of 81%. The NEQR and FRQI encoding schemes have an accuracy of 79% and 75%, respectively. The computational time for QCNN, FRQI and NEQR are considerably less than the conventional CNN method, which was tested by running the same dataset through DenseNet121 Keras architecture.