Skin Cancer Detection Using Artificial Neural Network
Skin Cancer Detection Using Artificial Neural Network
Skin Cancer Detection Using Artificial Neural Network
Thesis Presentation
4.2 PRE-PROCESSING
When Images are captured by cameras then there may be chance of noisy image so
before image processing noises are removed in preprocessing steps and images are
refined.
some Image processing strategies in conformity with the images. Thus pre-processing old
to referring in accordance with remove the undesirable applications about the skin or
post- processing referring after rise after the structure regarding image.
This part exhibit consequences a first part on composition yet 2nd portion about
decomposition together with executed distinct wavelet transforms.
5.3.4 Result of classification
These portion exhibit penalties a first portion on arrangement but 2nd piece respecting
decomposition together with rendered awesome wavelet transform.
Figure 5.6 Some training image results of detected skin cancer
5.3.4.1 Results of BNN classifier
Table.1 shows as a best result with highest overall accuracy is 90.2%. The best BNN is three
hidden layer with 40, 25 and 10 neurons for each hidden layer. The accuracy is increase
with number of neuron in hidden layer. However, number of hidden layer cannot improve
the result but it could reduce the probability of over-fitting.
5.3.4.2 Results of AANN classifier
The best AANN testing result found is 20 neurons in the first and third layer with overall
accuracy 81.5% as table 5.2 illustrated. Unlike BNN, ANN provides a stable classification
result in different number of neuron. However, when the layer 1 and layer 3 have different
size of neuron, the classifier result has a significant low accuracy diagnosing result
CONCLUSION & FUTURE WORK
This project reviewed the empirical consequences over
proposed algorithms of distinctive tiers regarding pores
and skin most cancers detection system. In pre-
processing stage, the outcomes exhibit the higher overall
performance on images. Then end result regarding
segmentation show a SRM image. In characteristic
extraction stage, show a arrangement and decomposition
images. Classification basic end result is 90.2 % for back-
propagation neural community yet 81.3% because of
auto-associative neural network. These outcomes are
between accidents along the effects obtained with the
aid of analytical solution.
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