## 3 Experimental Results and Discussion
### Comparison with State-of-the-Art  
The results of the performance comparison are summarized in Table 12 using the CE-MRI dataset. Due to the unavailability of the same split as used by other researchers, we have used (80:20)% split in addition to 10 fold (90:10)% for limited experimentation. [PERSON] et al. [43] used a stochastic algorithm resulting in an accuracy of 94.20%, indicating that a more exhaustive GA-based parametric search for CNN was required. [PERSON] et al. [44] used two CNN layers, with a uniform filter stack depth of 64 kernels in each layer and attained 91.43% accuracy. [PERSON] et al. [45] used extra input of tumor boundaries to improve the results by illustrating a capsule network (CapsNets) for brain tumor classification and reached an accuracy level of 89.56%. [PERSON] et al. [46] demonstrated the role of data preprocessing techniques to improve the CapsNets architecture for brain tumor \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Technique** & **Translation Source** & **Utility** & **Drawbacks** & **Compatible with Neuro Navigation System** \\ \hline Intraoperative & Optical fluorescence & & & The limited resolution, & \\ Intraoperative & imaging is based on the & & & quantification, depth of & \\ Fluorescence Imaging & accumulation of & In vivo assistance & penetration 5 mm, and & \\ (iFI) & fluorescence optical & during surgery [33] & availability of targeted contrast & \(\surd\) \\  & contrast media at the & & & agents with a high & \\  & & desired ROI & & signal-to-back-ground ratio & \\  & & & (SBR) [34] & & \\ \hline Intraoperative & IoUS uses & Integrated to the & Image quality subject to & \\ Ultrasound Imaging & high-frequency sound & neuro-navigation & & interoperation variability & \\ Ultrasound Imaging & waves as the viewing & system with accuracy & motion artifacts, and low & \(\surd\) \\ (IoUS) & source to form the & 1.40 \(\pm\) 0.45 mm & & image resolution restricts it for & \\  & image & (arithmetic mean) [35] & deep tumors [36] & & \\ \hline Intraoperative Raman & RS is based on light & Intraoperative & WHO says that its accuracy, & \\  & interaction within a & label-free molecular & & lower than 60\% for grade & \\ Spectroscopy (iRS) & material between its & information [5] with an & invasive cancer cells in the & \(\surd\) \\  & chemical bonds & accuracy of 1 mm [37] & normal brain or between grade & \\  & & & 3 and 4 gliomas & \\ \hline Hyperspectral Imaging & HSI is finding the & Image-guided surgery & & \\  & spectrum for each pixel & using an intraoperative & Penetration of depth, as well as & \\ (HSI) & in the image of a scene & visualization system for & real-time detection of tumors, & \(\surd\) \\  & [38] & delineation of the brain & & is a challenge & \\ \hline Intraoperative Magnetic & MRI is based on & It controls brain shift & Low magnetic field strength, & \\ Resonance Imaging & protons’ density & during resection of & contrast agents have their own & \\ (iMRI) & distribution in the brain & non-fluorescing & problems and need more & \(\surd\) \\  & & gliomas (\(\sim\)1 h) [4, 40] & research for FDA approved & \\ \hline Optical Coherence & OCT is based on the & It provides & & \\  & detection of light & micrometer-scale & & \\ Tomography (OCT) & backscattered by a & resolution with quick & Using ionizing radiation much & \(\surd\) \\  & tissue [41] & volumetric imaging & sensitive to the brain & \\ \hline UL-BTD Framework & Deep learning-based & Real-time brain tumor & & \\  & solution using artificial & surgical resection & & \\  & intelligence (AI) and & support, can be used & & \\  & iMRI scans & with desktop & & \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Comparison of different intraoperative neurosurgical navigation systems with the proposed method. classification with a classification accuracy of 92.60%. [PERSON] et al.