Brains are enormous and complex organs that control our nervous systems and contain about 100 billion nerve cells. The brain is an essential organ. A brain abnormality could put human health at risk. Tumors in the brain are among the most serious of these abnormalities. An uncontrollable growth of brain cells inside the skull causes this serious form of cancer. Generally, tumor cells exhibit heterogeneity, making them difficult to classify. In order to decide on the correct medication, it is essential that tumors are detected early, and their location, size, and types must also be assessed. Developing systems that incorporate human expertise is becoming increasingly popular using Soft Computing. Image processing and cytology are used more often to diagnose disease. Correct diagnosis is essential in treating and curing diseases. This paper proposes a fuzzy logic based brain tumor classification method that can be used for proper treatment planning. This paper provides detailed analysis of the advantages of the hybrid method, demonstrating the fact that when Neuro-Fuzzy Neural is paired with MResNet (NFMResnet), there is a significant increase in classification accuracy. The NFMResnet contains convolutional layers, pooling layers, and fully-connected layers, as well as a Fuzzy Self-Organization Layer. Using MResNet and fuzzy logic, the model handles uncertain and imprecise input patterns. Three independent steps are involved in training the NFMResnet. Three independent steps are involved in training the NFMResnet.