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A Neuro-Fuzzy Based Intelligent System For Diagnosis Of Renal Cancer

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Figure

Figure 2: Architecture of Fuzzy inference system.
Figure 3: DEVELOPMENT
figure 6, figure 7 and figure 8 shown the membership functions for input1, input2, input3 and input4 respectively
Figure 9: Training error at 3 epochs.  5. RESULT phase. The performance is also evaluated by considering various parameters such as sensitivity, specificity, precision and classification accuracy

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