Robust Slidingmode Control Design for Voltage Regulated Quadratic Boost Converter
Classification of skin lesions plays a crucial role in diagnosing various, local and gene related, medical conditions in the field of dermoscopy. Estimation of these biomarkers are used to provide some insight, while detecting cancerous cells and classifying the lesion as either benign or malignant. This paper presents groundwork for detection of skin lesions with cancerous inclination by segmentation and subsequent application of Neural Network on dermoscopy images, Images with skin lesions were segmented based on individual channel intensity Thresholding. The resultant images were fed into NN for feature extraction. The extracted features were then used for classification by an NN classifier. Previously, several approaches have been used for subject diagnostic with varying degree of success. However, room is still available for exploring other techniques for improving proportion of successfully detected malignant lesions.Robust Slidingmode Control Design for Voltage Regulated Quadratic Boost Converter
- Principal Component Analysis
- Local binary pattern and shape features
- KNN and FNN classifier
Draw backs of Existing method:
- High Computational load and poor discriminatory power.
- LBP doesn?t differentiate the local texture region.
- FNN is slow training for large feature set.
- Less accuracy in classification
Skin lesion classification for Computer Aided Diagnosis (CAD) system based on,
- Hybrid features involves color features and texture descriptors
- Neural Network classifier
- Color Space Conversion
- GLCM Features Extraction and ABCD Parameters
- DRLBP (Discriminative Robust Local binary Pattern)
- NN Training and Classification
- Adaptive thresholding
- DRLBP has better discriminatory power
- NN is fast and better compatible in classification.
- Low computational complexity
- Skin cancer diagnosis support system in Health care fields
- Matlab2014a and above versions
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