Facial Recognition Approach using ABCD Algorithm for Cancer Treatment
M. Aruna1, B. Arthi2, G. Padmapriya3

1M. Aruna, Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, (Tamil Nadu), India.
2B. Arthi, Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, (Tamil Nadu), India.
3G. Padmapriya, Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, (Tamil Nadu), India.

Manuscript received on 03 July 2019 | Revised Manuscript received on 10 July 2019 | Manuscript published on 30 July 2019 | PP: 3408-3411 | Volume-8 Issue-9, July 2019 | Retrieval Number: H7142068819/19©BEIESP | DOI: 10.35940/ijitee.H7142.078919

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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Malignant melanoma is one of the generally known cancers due to the changes in the skin behaviour that cause a drastic increase in numerous melanomas which is seen among many white-skinned people. To detect and classify skin lesions, we require a fast and reliable system. The face detection algorithms are used in which, an image dataset is formed and from that several images are tested for the presence of a face. When the face is present, the image is selected for further processing and separate features are detected. The presence of the face, along with two eyes, nose, mouth and lips are necessary for the face detection to work efficiently. A specific area of the face is selected as a test case and the skin irregularity is checked for abnormal features are present or not. An algorithm by the name Asymmetry, Border, Color and Dermatoscopic features (ABCD) is developed which will check the skin parameters and help figure out the presence of abnormal growth. The accuracy of detection will depend upon the clarity of the input image, the brightness and the sharpness. The later part of the project will stress the importance of data exports from the working data sets to a portable format.
Keywords: Medical Imaging, Facial Recognition, ABCD Algorithm, Cloud Computing, Feature Selection

Scope of the Article: Pattern Recognition