# Multiscale Analysis for Improving Texture Classification
## 4 Experimental Results
### Experiments with HI Datasets  
Table 6 presents the accuracy achieved by monolithic classifiers and ensemble methods, both trained with the proposed method on the CRC dataset with TiO and BiT, GLCM, and Haralick descriptors, individually. The proposed approach provided its best accuracy of 94.71%, with the HistoB and LightB ensemble models with all feature descriptors. The accuracy difference between TiO and the first best-related work was nearly 2.00%, which corroborated the discriminating ability of our method. Additionally, compared to each descriptor that TiO is made up of, TiO outperformed all of them when employed individually. Table 7 compares the results achieved by the proposed approach with some state-of-the-art works to assess its effectiveness. The results achieved by TiO on the CRC dataset showed that the proposed approach worked well on images with other structures apart from textures and with no need for data augmentation. Moreover, CNNs need to be trained \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Texture Descriptors**} & \multicolumn{4}{c}{**Classification Algorithms**} \\ \cline{2-5}  & **HistoB** & **LightB** & **LDA** & **CatBoost** \\ \hline TiO & **94.71** & **94.71** & 94.37 & 93.80 \\ \hline TiO-CV & 94.28 (0.017) & **94.50 (0.019)** & 93.37 (0.014) & 93.87 (0.018) \\ \hline BiT & 93.22 & 92.61 & 86.67 & 92.92 \\ \hline GLCM & 85.98 & 86.97 & 81.79 & 87.20 \\ \hline Haralick & 91.62 & 91.31 & 92.38 & 91.39 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Accuracy (%) of monolithic classifiers and ensemble methods with TiO and each descriptor employed individually on the test set of the CRC dataset. TiO (CV) represents the average accuracy and standard deviation (%) on a 10-fold cross-validation setup. The best results are shown in boldface. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference** & **Approach** & **Accuracy** \\ \hline \multirow{3}{*}{[PERSON] [9]} & GLCM & 86.83 \\  & [PERSON] & 94.89 \\  & BiT & 97.87 \\ \hline [PERSON] et al. [32] & [PERSON] & 91.20 \\ \hline [PERSON] et al. [33] & [PERSON] & 97.73 \\ \hline [PERSON] et al. [34] & [PERSON] & 99.01 \\ \hline TiO + LDA & Shallow & **100.0** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Accuracy (%) of the proposed method and related works on the KTH-TIPS dataset. The best result is shown in boldface. with a large quantity of labeled data, which may be prohibitive in medical imaging and other related fields. Table 8 shows the accuracy achieved by monolithic classifiers and ensemble methods trained with all feature descriptors (TiO). For the the BreakHis dataset, the HistoB ensemble model achieved the best accuracy for 40\(\times\), 100\(\times\), and 400\(\times\) magnifications, and the LightB ensemble model for a 200\(\times\) magnification. Table 9 shows and compares the results achieved by the proposed approach using all feature descriptors with the state of the art for the BreakHis dataset. One can note that the proposed approach achieved a considerable accuracy of 98.64% with all feature descriptors for a 40\(\times\) magnification, which slightly outperformed the accuracy of both shallow and deep methods. The difference in accuracy between the proposed approach and the second-best method (CNN) was nearly 1% for a 40\(\times\) magnification. Likewise, the proposed method achieved a considerable accuracy of 97.85%, 98.76%, and 98.22% for 100\(\times\), 200\(\times\), and 400\(\times\) magnifications, respectively, which slightly outperformed the second-best method with a difference of 0.9% for 100\(\times\), 1.05% for 200\(\times\), and 1.0% for 400\(\times\) magnifications, respectively. We also conducted additional experiments separately with the BiT, GLCM, and Haralick descriptors. For all the magnifications, TiO outperformed each descriptor with the maximum accuracy difference of 1.25%, 0.48%, 1.01%, 1.5% for 40\(\times\), 100\(\times\), 200\(\times\), and 400\(\times\) magnifications, respectively.