Robust Slidingmode Control Design for Voltage Regulated Quadratic Boost Converter

Description

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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.

Existing Method

  • 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

Proposed Method

Skin lesion classification for Computer Aided Diagnosis (CAD) system based on,

  • Hybrid features involves color features and texture descriptors
  • Neural Network classifier

Methodologies

  • Color Space Conversion
  • GLCM Features Extraction and ABCD Parameters
  • DRLBP (Discriminative Robust Local binary Pattern)
  • NN Training and Classification
  • Adaptive thresholding

Advantages

  • DRLBP has better discriminatory power
  • NN is fast and better compatible in classification.
  • Low computational complexity

Application

  • Skin cancer diagnosis support system in Health care fields

Software Requirement

  • Matlab2014a and above versions

REFERENCES

[1] Omar Abuzaghleh, Buket D.Barkana,Miad Faezipour, ?Noninvasive Real-Time Automated Skin Lesion Analysis System for Melanoma Early Detection and Prevention? LATEX, IEEE Journal of Transactional Engineering in Health and Medicine(2015), IEEE APRIL 2015.

[2] Kiran Ramlakhan, Yi Shang, ?A mobile Automated Skin Lesion Classification System? LATEX, IEEE International Conference on Tools with Artificial Intelligence(2011), IEEE 2011.

[3] Alexandros Karargyris,Orestis Karargyris,Alexandros Pantelopoulos, ?Derma/care:An advanced image-Processing mobile application for monitoring skin cancer? LATEX, IEEE International Conference on Tools with Artificial Intelligence(2012), IEEE 2012.

[4] Tarun Wadhawan,Ning Situ,Keith Lancaster, ?SkinScan:A Portable Library For Melanoma Detection On HandHeld Devices? LATEX, IEEE International Conference on Tools with Artificial Intelligence(2011), IEEE 2011.

[5] Omar Abuzaghleh,Buket D. Barkana,Miad Faezipour, ?SKINcure: A Real Time Image Analysis System to Aid in the Malignant Melanoma Prevention and Early Detection? LATEX, IEEE Journal of Transactional Engineering in Health and Medicine(2014), IEEE APRIL 2014.

[6] Qaisar Abbas , Irene FondoGarcia , M. Emre Celebi and Waqar Ahmad, ?A Feature.-Preserving Hair Removal Algorithm for Dermoscopy Images? LATEX, IEEE Journal of Skin Research and Technology(2011), IEEE March 2011.

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