## 6 Conclusions  
A system based on deep learning and machine learning methods for segmenting the wounds from daily patient photographs and classifying each post-surgical site as altered or not altered to prevent the risk of infection was designed and developed. The proposed system consists of two separate models, wound segmentation and wound classification, combining DL and traditional ML techniques. The segmentation model extracts the wound's region areas and categorizes each of those regions with the corresponding wound type. This segmentation architecture showed promising results, with 89.9% of mean IoU after post-processing and a 90.1% mean average precision. A group of color and textural features was extracted from the output region of interest to feed one of the three wound-type classifiers that reached the final binary decision. Separating the final classification into separate classifiers was more effective than a single classifier for all the wound types. After hyperparameter search and model selection, the selected ML algorithms were RF, SVM, and KNN for CW, DW, and LW classifiers, respectively. The models were optimized in function of the F2 in order to favor the recall metric and of the F1 to find a balance solution with precision and recall. The best classifier (LW) obtained an 87.6% recall, 52.6% precision, 65.7% F1, and 77.3% F2. The appearance of FN could be a big issue for the system since it could overlook wound alterations and detect them negatively, preventing the intervention of the clinicians and increasing the risk of developing an infection. As such, the optimization with F2 was favored instead of F1. Conceptualization, C.P., F.G.-F., R.V., P.C., J.F. and A.L. ; methodology, C.P. ; software, C.P. ; validation, C.P. and F.G.-F.; formal analysis, C.P. ; investigation, C.P. and F.G.-F.; data curation, C.P. writing--original draft preparation, C.P. ; writing--review and editing, All Authors; visualization, C.P. ; supervision, F.G.-F.; project administration, A.L. ; funding acquisition, P.C., J.F. and A.L. All authors have read and agreed to the published version of the manuscript. This research has been supported by Fundacao para a Ciencia e Tecnologia (FCT) under CardioFollowo.AI project (DSAIPA/AI/0094/2020), Lisboa-05-3559-FSE-000003 and UIDB/04559/2020. The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board (or Ethics Committee) of Centro Hospitalar Lisboa Central (protocol code CA2651 2020/06/08). Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: The dataset generated during and/or analyzed during the current study is available from the corresponding author on reasonable request. **Table A2. **_Cont._ \begin{tabular}{c c} \hline
**Feature Name** & **Equation** \\ \hline Kurtosis & \(\sum_{i}\left(\dfrac{i-\mu}{\sigma}\right)^{4}\)H\({}_{i}\) \\ Energy & \(\sum_{i}H_{i}^{2}\) \\ Minimal Grey Level & \(min\{f(x,y)\}*\) \\ Maximal Grey Level & \(max\{f(x,y)\}*\) \\ Coefficient of Variation & \(\dfrac{\sigma}{\mu}\) \\ Percentiles (10, 25, 75, 90) & \(f_{n}=\sum_{i=0}^{f_{n}}H_{i}=c*\) \\ Histogram Width & \(f_{4}-f_{1}\) \\ \hline \end{tabular}   
* Considering \(f(x,y)\) is a grayscale image and \(H_{i}\) is the first order histogram defined as:  
\[H_{i}=\dfrac{number\ of\ pixels\ with\ grey\ level\ i\ inside\ ROI}{total\ number\ of\ pixels\ in\ the\ ROI}\] (A17)  
** where \((n,c)=(1,0.1),(2,0.25),(3,0.75),(4,0.9)\). Note that 50-Percentile is the median  
**Table A3. ** The gray-level co-occurrence matrix (GLCM) textural features used in this study. \begin{tabular}{c c} \hline
**Feature Name** & **Equation** \\ \hline Angular Second Moment & \(f_{1}=\sum_{i=0}^{N-1}\sum_{i=0}^{N-1}p(i,j)^{2}\) \\ Contrast & \(f_{2}=\sum_{i=0}^{N-1}n^{2}\left\{\sum_{i=0|i-j|=n}^{N-1}\sum_{j=0}^{N-1}P(i,j)\right\}\) \\ \hline \end{tabular}