C H A P T E R 6 . C O N C LU S I O N
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, WL showed good results with a 87.6% recall, 52.6% precision, 65.7% F1 and 77.3% F2. However, the worst classifier, WD, achieved low scores for the proposed system to be reliable, with a 68.4% recall, 33.2% precision, 44.7% F1 and 56.4% F2.
The appearance of FNs could be a big issue for the system, since it could overlook through wound alterations and detect them negatively, preventing the intervention of the clinicians on the matter and increasing the risk of developing an infection. As so, the optimization with F2 was favored instead of F1. However, since the system intended to reduce the workload of the clinical team, the solution needs to be improved with a focus on finding a higher recall and precision system in order to simultaneously give right predictions and reduce the time spent by the clinical team on the task. For an automatic system to be implemented in healthcare, it needs to be highly reliable since a bad prediction can have serious consequences for the patients. Therefore, the application of the developed system needs to be improved in order to be implemented on a real clinical context but the proposed pipeline was proven effective for the proposed task.
6 . 3 . F U T U R E WO R K
same parameters, since each model can have a different behaviour. Additionally, other hy-perparameters could be implemented to optimize even more the model, such as learning rate, momentum, adding dropout and changes in the architecture.
For improving the ML algorithm, other features, such as SIFT and HOG, could be explored and extracted to verify their variance between the two classes and increasing the performance of the classification model. Besides, other undersampling techniques could be employed with the combination of SMOTE, such as Tomek Links and Edited Nearest Neighbour.
Another possible solution would be a hybrid approach that extracts the features from the CNN layers and later feed them into a ML algorithm.
In addition to improving some specific things from the proposed system, it would be interesting to do a time evolution system, that for each patient compares the daily image with the previous ones, to verify if the healing process regressed by analyzing if the wound shows more redness or abnormal coloration. However, this system would need some embedded memory for saving the pictures from the previous days.
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A
Wound segmentation
A.1 Initial model elimination
Table A.1: Model architectures used for training the reduced dataset.
Model name Decoder Encoder
Fcn-8 Vanilla CNN FCN8
Fcn-32 Vanilla CNN FCN8
PspNet Vanilla CNN PspNet
Unet Vanilla CNN Unet
Vgg-Unet VGG16 Unet
ResNet50-Unet ResNet50 Unet MobileNet-Unet MobileNet Unet SegNet Vanilla CNN SegNet ResNet50-Segnet ResNet50 SegNet MobileNet-Segnet MobileNet SegNet
Table A.2: Results of the mentioned models with a training set of five images and test set of two images.
Model name Mean IoU
Fcn-8 28.1%
Fcn-32 18.0%
PspNet 28.9%
Unet 13.7%
Vgg-Unet 27.9%
ResNet50-Unet 37.7%
MobileNet-Unet 44.3%
Segnet 16.0%
ResNet50-SegNet 41.9%
MobileNet-SegNet 37.8%
B
Equations for feature extraction
B.1 Color features
The following list represents the equations of color features:
• Mean
Ei=
N
X
j=1
1
NPij (B.1)
• Standard deviation
σi= vu ut
1 N
N
X
j=1
(pij−Ei)2 (B.2)
• Skewness
skew= m3
m3/22 where mi= 1 N
XN n=1
(pixel(n)−mean)i (B.3)
• Energy
E= 1 N
N
X
i=1
pixel(i)2 (B.4)
B.2 Textural features
B.2.1 First order statistics features
Consideringf(x, y) is a greyscale image andHiis the first order histogram defined as:
Hi=number of pixels with grey level i inside ROI
total number of pixels in the ROI (B.5) The FOS parameters are illustrated in Table B.1.
A P P E N D I X B . E Q UAT I O N S F O R F E AT U R E E X T R AC T I O N
Table B.1: Parameters of FOS.
Parameters fN
Mean f1=µ=P
iiHi Standard Deviation f2=σ=pP
i(i−µ)2Hi
Median f3=Pf3
i=0Hi= 0.5 Mode f4=argmaxi{Hi}
Skewness f5=P
i
i−µ σ
3
Hi
Kurtosis f6=P
i
i−µ σ
4
Hi
Energy f7=P
iHi2 Minimal Grey Level f9=min{f(x, y)} Maximal Grey Level f10=max{f(x, y)} Coefficient of Variation f11=σµ Percentiles (10, 25, 75, 90) fn=Pfn
i=0Hi=c∗ Histogram Width f16=f15−f12
*where (n, c) = (12,0.1),(13,0.25),(14,0.75),(15,0.9). Note that 50-Percentile is the median
B.2.2 GLCM features
The texture measures that can be extracted from the GLCM matrices are described below, where the needed notation is displayed in Table B.2
• Angular Second Moment =Energy2 f1=
N−1
X
i=0 N−1
X
i=0
p(i, j)2 (B.6)
• Contrast
f2=
N−1
X
i=0
n2
N−1
X
i=0|i−j|=n N−1
X
j=0
P(i, j)
(B.7)
• Correlation
f3=
N−1
X
i=0 N−1
X
j=0
i−µx σx
! j−µy σy
!
p(i, j) (B.8)
• Sum of Squares: Variance
f4=
N−1
X
i=0 N−1
X
i=0
(i−µ)2p(i, j) (B.9)
92
B . 2 . T E X T U R A L F E AT U R E S
• Homogeneity
f5=
N−1
X
i=0 N−1
X
i=0
p(i, j)
1 +|i−j| (B.10)
• Sum Average
f6=
2N−1
X
k=1
kpx+y(k) (B.11)
• Sum Variance
f7=
2N−1
X
k=1
(i−µx−y)2px+y(k) (B.12)
• Sum Entropy
f8=−
2N−1
X
k=1
px+y(k)ln[px+y(k)] (B.13)
• Entropy
f9=−
2N−1
X
i=0 N−1
X
i=0
p(i, j)log[p(i, j)] (B.14)
• Difference Variance
f10=
N−1
X
k=0
(k−µx−y)2px−y(k) (B.15)
• Difference Entropy
f11=−
N−1
X
k=0
px−y(ik)log[px−y(k)] (B.16)
• Information Measures of Correlation
f12=HXY −HXY1
max{HX, HY} (B.17)
f13= (1−exp(−2.0[HXY2−HXY]))1/2 (B.18) Where,
HX=−PN−1
i=0 px(i)log[px(i)]
HY =−PN−1
j=0 py(j)log[py(j)]
HXY =−PN−1 i=0
PN−1
j=0 p(i, j)log[p(i, j)]
HXY1 =−PN−1 i=0
PN−1
j=0 p(i, j)log[px(i, j)py(i, j)]
HXY2 =−PN−1 i=0
PN−1
j=0 p(i, j)log[px(i, j)py(i, j)]2
A P P E N D I X B . E Q UAT I O N S F O R F E AT U R E E X T R AC T I O N
• Maximal Correlation Coefficient
f14= (Second largest Eigenvalue of Q)1/2 (B.19) Where,
Q(i, j) =PN−1 k=0
p(i,k)p(j,k) px(i)py(k)
Table B.2: GLCM notation.
Notation Meaning p(i, j) PN−1P(i,j)
i=0 PN−1 j=0 P(i,j)
N number of grey levels px(i) PN−1
i=0 p(i, j) py(i) PN−1
j=0 p(i, j)
µx PN−1
i=0 ipx(i)
µy PN−1
j=0 jpy(j) σy2 PN−1
i=0 (i−µx)2px(i) σy2 PN−1
j=0(j−µy)2py(j) px+y(k) PN−1
i=0
PN−1
j=0,i+j=kp(i, j)) px−y(k) PN−1
i=0
PN−1
j=0,|i−j|=kp(i, j)) µx+y P2N−1
k=1 kpx+y(k) µx−y PN−1
k=1 kpx−y(k)
94