Skin Cancer Detection Using Matlab
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Human Cancer is one of the most dangerous disease which is mainly caused by genetic instability of multiple molecular alterations. Among many forms of human cancer, skin cancer is the most common one. To identify skin cancer at an early stage we will study and analyze them through various techniques named as segmentation and feature extraction. Here, we focus malignant melanoma skin cancer, (due to the high concentration of Melanoma- Hier we offer our skin, in the dermis layer of the skin) detection. In this, We used our ABCD rule dermoscopy technology for malignant melanoma skin cancer detection. In this system different step for melanoma skin lesion characterization i.e, first the Image Acquisition Technique, pre-processing, segmentation, define feature for skin Feature Selection determines lesion characterization, classification methods. In the Feature extraction by digital image processing method includes, symmetry detection, Border Detection, color, and diameter detection and also we used LBP for extract the texture based features. Here we proposed the Back Propagation Neural Network to classify the benign or malignant stage.
- 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
- ANN-Back Propagation Neural Network classifier
- Color Space Conversion
- GLCM Features Extraction and ABCD Parameters
- BPN Training and Classification
- Adaptive thresholding
- PNN is fast and better compatible in classification.
- Low computational complexity
- Skin cancer diagnosis support system in Health care fields
- Matlab7.5 and above versions
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