Python
Abstract:
MOBILE devices, including smart phones, are being used by billions of people all around the world. This creates the opportunity to design a wide variety of mobile image applications, Among many imaging applications, healthcare applications have drawn a lot of attentions recently. In the existing design a detection system that is optimized to run entirely on the resource constrained smart phone. Our system intends to localize the skin lesion by combining a lightweight method for skin detection with a hierarchical segmentation approach using two fast segmentation methods. In the proposed system Statistical Confidence Intervals based segmentation model is designed henceforth the classification is further done by resilient neural network(RNN) to improve the quality of classification .A morphological study is been developed here for deep understanding of the image features etc.
Proposed Design:
In the proposed system Statistical Confidence Intervals based segmentation model is designed henceforth the classification is further done by resilient neural network(RNN) to improve the quality of classification .A morphological study is been developed here for deep understanding of the image features etc.
Proposed Solution:
- Early detection of disease
- Accuracy will be improved
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