## 5 Discussion  
Since the 2000s, deep learning AI techniques have become very popular for diverse applications due to their high performance [37, 38, 39]. They have been applied in the biomedical field with notable success [40, 41, 42, 43]. Some of these algorithms can veritably be implemented in the clinical environment as medical decision support systems to assist physicians and/or paramedical personnel. Unfortunately, deep learning models are computationally intensive \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Data** & **Accuracy** & **Sensitivity** & **Specificity** & **Precision** & **F-Score** & **MCC** \\ \hline Axial & 98.35 \(\pm\) 0.02 & 96.45 \(\pm\) 0.03 & 99.59 \(\pm\) 0.01 & 99.36 \(\pm\) 0.02 & 97.88 \(\pm\) 0.02 & 96.58 \(\pm\) 0.04 \\ Sagittal & 97.74 \(\pm\) 0.019 & 95.00 \(\pm\) 0.044 & 99.79 \(\pm\) 0.054 & 99.71 \(\pm\) 0.075 & 97.30 \(\pm\) 0.024 & 95.45 \(\pm\) 0.03 \\ Hybrid & 98.20 \(\pm\) 0.06 & 96.38 \(\pm\) 0.06 & 99.49 \(\pm\) 0.06 & 99.26 \(\pm\) 0.04 & 97.80 \(\pm\) 0.05 & 96.32 \(\pm\) 0.012 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The general classification results (%) using a Fine kNN classifier with 10-fold cross-validation \(\pm\) standard deviations. Figure 3: Confusion matrices for binary classification using Fine kNN classifier. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Data** & **Accuracy** & **Sensitivity** & **Specificity** & **Precision** & **F-Score** & **MCC** \\ \hline Axial & 98.37 & 96.46 & 99.60 & 99.37 & 97.89 & 96.59 \\ Sagittal & 97.75 & 95.01 & 99.80 & 99.72 & 97.31 & 95.46 \\ Hybrid & 98.22 & 96.39 & 99.50 & 99.27 & 97.81 & 96.34 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Calculated performance results (%) per classifier used. and demand high time costs for parameter tuning. This study proposed a computationally lightweight machine learning model with handcrafted feature engineering for diagnosing MS on FLAIR brain images. The performance of the proposed model was compared with the results of 19 pre-trained models, including deep learning and transfer learning models, that were also trained and tested on a common prospectively acquired brain FLAIR MRI dataset comprising three subsets of images in different orientations. Fixed-sized patches with a size of 32 \(\times\) 32 have been used to extract deep features using transfer learning. After extracting features from individual images in the study data subsets using the ExMPLPQ algorithm and the 19 pre-trained models, kNN classifier with 10-fold cross-validation was used to perform binary classification of the input images into MS vs. non-MS classes. The proposed ExMPLPQ attained excellent performance with >97% accuracy across all three study data subsets (Table 2). In contrast, the accuracy results after 100 classification runs of the 19 pre-trained models, which incorporated between 1.24 million and 144 million parameters, were all inferior to our model (Table 4). Among the 19 pre-trained models, Efficient b0 had the highest accuracy rates of 93.69%, 90.26%, and 93.22% for the axial, sagittal, and hybrid images, respectively. In contrast, GoogleNet attained the lowest corresponding accuracy rates of 84.49%, 82.02%, and 85.62. Table 5 shows comparative results (classification accuracies) for transfer learning methods. We performed a non-systematic review of the literature on methods related to the automated classification of MS, are summarized in Table 5. Most of the methods in the literature relied on deep learning, especially convolutional neural network (CNN) models, to attain high classification results. In contrast, we presented a handcrafted feature-engineering machine learning model for detecting MS on brain MRI. Our ExMPLPQ model attained over 97% binary classification accuracy on all three study data subsets. Only [PERSON] et al. [58] attained a higher classification performance than our model, but their dataset had fewer subjects.