# Accurate MRI-Based Brain Tumor Diagnosis: Integrating Segmentation and Deep Learning Approaches
## 4 Results
Table 2 presents a comprehensive evaluation of the proposed method using various datasets, including the Brain Tumor Segmentation Challenge (BRATS) series from 2013 to 2019, the Alzheimer's Disease Neuroimaging Initiative (ADNI), The Cancer Imaging Archive (TCIA), and BrainWeb. These datasets were employed to train models on a broad spectrum of realistic neuroimaging data. As detailed in Table 2, the proposed method exhibits an exemplary performance in tumour detection using the Multi-Support Vector Machine (M-SVM) classifier, particularly with the BRATS 2018 dataset. Table 3 demonstrates that the proposed brain tumour detection method performs exceptionally well with M-SVM classification. \begin{table}
\begin{tabular}{c c c c} \hline \hline  
The values reported indicate the high effectiveness of the method, with an accuracy of 97.47%, demonstrating the model's ability to identify both positive and negative cases correctly. The sensitivity is recorded as 97.22%, indicating the model's proficiency in correctly identifying positive cases. The specificity is 97.94%, reflecting the model's accuracy in identifying negative cases. Finally, the Dice coefficient index, which measures the similarity between the predicted and actual labels, is 96.71%. These metrics collectively suggest that the proposed method exhibits robust performance in detecting brain tumours. Table 4 provides a comparative analysis of various methodologies for brain tumour classification using the BRATS 2018 dataset. The table outlines the classification accuracies of different techniques, facilitating an evaluation of their efficacy in tumour detection. The methods include combinations of CNN and the Local Binary Patterns (LBP) with Particle Swarm Optimization (PSO) reported by [PERSON] et al. [44], achieving a 92.5% accuracy. The LSTM by [PERSON] et al. [51] shows slightly higher accuracy at 93.8%. The brain-storm optimisation technique by [PERSON] et al. [52] matches the first at 92.5%. At the same time, a method integrating Discrete Cosine Transform (DCT), CNN, and the Extreme Learning Machine (ELM) by [PERSON] et al. [53] achieves a value of 93.4%. Notably, the proposed model, which utilises a CNN, MobileNetV2, and Multi-Support Vector Machine (M-SVM), significantly surpasses these, recording a classification accuracy of 97.5%. This indicates \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Preprocessing** & **Model** & **Performance** \\ \hline BRATS & Intensity normalisation, & & DCNN & BRATS 2018: DSC 86.2\%, SEN 84.8\%, \\
2018 & rhinghest and lowest & (Dense-MultiOCM 4) & SPE 99.5\% \\ \hline TCIA & rhinginging, padding, resizing, intensity normalisation & U-Net & DSC 84\%, SEN 92\%, \\ \hline BRATS & rhinginghest & & SPEC 92\%, \\
2013, & - & AFPNet 5 + 3D CRF & BRATS 2013 DSC 86\%, \\
2015, & & & BRATS 2015 DSC 82\%, \\
2018 & & & BRATS 2018 86.58\% \\ \hline BRATS & rhinghest & Inception-based U-Net & \\
2015, & Z-score normalisation & + up skip connection + cascaded training & DSC 89\%, PR 78.5\%, SEN 89.5\% \\
2017 & & & strategy \\ \hline BRATS & rhinghest & & \\
2015, & rhinghest & & BRATS 2015: DSC 85\%, \\
2015 & rhinghest & U-Nets (TIU-Net) & BrainWeb DSC 99.5\% \\ BrainWeb & (BrainWeb) & & \\ \hline BRATS & rhinghest & & \\
2015 & - & LSTM multi-modal & DSC 73.09\%, SEN 63.76\%, \\
2015 & - & U-Net & PR 89.79\% \\ \hline \hline \end{tabular} \({}^{1}\) Heterogeneous CNN combined with conditional random fields and recurrent regression-based neural algorithm.  
\end{table} 
Table 2: _Cont._ \begin{table}
\begin{tabular}{c c} \hline \hline
**Evaluation Metrics** & **Performance** \\ \hline Accuracy & 97.47\% \\ \hline Sensitivity & 97.22\% \\ \hline Specificity & 97.94\% \\ \hline Dice coefficient index & 96.71\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Evaluation of proposed method. the superior performance of the proposed method in accurately classifying brain tumours, highlighting its potential utility in clinical application.