## 7 Concluding Remarks  
We proposed a method for cancer identification exploring texture properties taken from microphotographs of rat liver. For this, we used the LPQ spectral texture operator, a widely used descriptor, especially when the images may be affected by blur, a noise that typically occurs in images such as those used in this work. We also experimented with GBD, and lastly, we investigated the complementarity between classifiers created in both scenarios by using late fusion strategies. Experiments performed on a dataset created by researchers from Enteric Neural Plasticity Laboratory of the State University of Maringa confirm the efficiency of the proposed strategies in isolation. In addition, we noticed an important level of complementarity between the classifiers created using both descriptors experimented. The best result obtained using LPQ was 91.16% of accuracy. In this way, it is possible to state that cancer can be identified in the Walker 256 tumor model using the LPQ texture operator with reasonably good rates, answering **RQ1**. For GBD, the best result obtained was 96.67% of accuracy, which responds positively to **RQ2**. Finally, the best overall result was obtained combining classifiers created using both LPQ and GBD descriptors, achieving 99.16% of accuracy. Thus, we can state that **RQ3** was also positively answered. Finally, we make a brief comment regarding the main limitation of this work. As happens in several works that deal with biomedical images, the main difficulty faced here refers to the limited size of the dataset, which makes it more difficult to create a more robust model and to make comparisons. Aiming to mitigate this issue, we performed the Friedman statistical test, and we confirmed that there is a meaningful difference between the results obtained by combining both strategies investigated here and the results obtained by each strategy in isolation. As future work, we intend to expand our investigations using an additional dataset currently under development. This dataset is also being created by researchers from Enteric Neural Plasticity Laboratory of the State University of Maringa. In this new version of the dataset, two new classes will be included: treated control and treated Walker 256 tumor. Other tests using granulocyte, such as pattern spectrum and others, are also planned to be made. Conceptualization, M.F.T.C., S.A.S.J., F.C.F., J.V.C.M.P., J.N.Z. and Y.M.G.C. ; Data curation, C.C.O.B. ; Funding acquisition, Y.M.G.C. ; Investigation, M.F.T.C., S.A.S.J. and F.C.F. ; Methodology, M.F.T.C., S.A.S.J., F.C.F. and Y.M.G.C. ; Project administration, F.C.F. and Y.M.G.C. ; Supervision, F.C.F. and Y.M.G.C. ; Validation, M.F.T.C., S.A.S.J., C.C.O.B., F.C.F., J.V.C.M.P. and Y.M.G.C. ; Visualization, J.N.Z. ; Writing--original draft, M.F.T.C., S.A.S.J., F.C.F. and Y.M.G.C. ; Writing--review and editing, M.F.T.C., S.A.S.J., F.C.F., J.V.C.M.P., J.N.Z. and Y.M.G.C. All authors have read and agreed to the published version of the manuscript. This research has been partly supported by the Brazilian agencies National Council for Scientific and Technological Development (CNPq) and Coordination for the Improvement of Higher Education Personnel (CAPES). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Fold** & **Late Fusion** & **BinAC–SVM** & **BinAO–SVM** & **LPQ\({}_{7}\)–SVM** \\ \hline f1 & 1 & 2.5 & 4 & 2.5 \\ f2 & 1.5 & 1.5 & 3 & 4 \\ f3 & 1.5 & 1.5 & 3.5 & 3.5 \\ f4 & 1.5 & 1.5 & 3 & 4 \\ f5 & 1.5 & 1.5 & 4 & 3 \\ f6 & 1.5 & 4 & 1.5 & 3 \\ \hline
**Average** & 1.416 & 2.083 & 3.166 & 3.333 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Classifiers ranking. **Institutional Review Board Statement:** Ethical review and approval were waived for this study due to the use of rats during the image acquisition phase of the dataset construction. The study follows the ethical principles under the terms set out in the Brazilian federal Law 11,794 (October 2008) and the Decree 66,689 (July 2009) established by the Brazilian Society of Science on Laboratory Animals (SBCAL).