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Research Article

Brain Tumor Classification Based on Hybrid Optimized Multi-features Analysis Using Magnetic Resonance Imaging Dataset

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Article: 2031824 | Received 22 Oct 2021, Accepted 18 Jan 2022, Published online: 13 Feb 2022

Figures & data

Table 1. Procedural Steps of the HBTC framework

Figure 1. Sample Images of Four Brain Tumor Types.

Figure 1. Sample Images of Four Brain Tumor Types.

Figure 2. Sample Effects of Image Preprocessing.

Figure 2. Sample Effects of Image Preprocessing.

Table 2. TACS Model

Figure 3. Overall Model of TACS Methodology with taking ROI.

Figure 3. Overall Model of TACS Methodology with taking ROI.

Table 3. F+ MI+PA+CFS based selected features

Figure 4. The Complete HBTC Framework.

Figure 4. The Complete HBTC Framework.

Table 4. Parameters values of MLP

Table 5. HBTC Machine Vision Classifiers based on 10 × 10 ROI

Table 6. Confusion Matrix for MLP based on 10 × 10 ROI

Table 7. HBTC Machine Vision Classifiers based on 15 × 15 ROI

Table 8. Confusion Matrix for J48 based on 15 × 15 ROI

Table 9. HBTC Machine Vision Classifiers based on 20 × 20 ROI

Table 10. Confusion Matrix for MLP based on 20 × 20 ROI

Figure 5. Comparison Graph of MLP on ROIs of sizes 10 × 10, 15 × 15 and 20 × 20.

Figure 5. Comparison Graph of MLP on ROIs of sizes 10 × 10, 15 × 15 and 20 × 20.

Figure 6. Comparison Graph of Overall Classicification Performance of Four Classifers.

Figure 6. Comparison Graph of Overall Classicification Performance of Four Classifers.

Table 11. Comparison of HBTC framework with other streamed classification techniques