Improved Technique to Diagnose Skin Cancer using Advanced Image Processing and Machine Learning Techniques
Ashima Kalra1, Khushmeen Brar2, Piyush Samant3
1Ashima Kalra, PHD Research Scholar, I. K. Gujral Punjab Technical University, Jalandhar (Punjab), India.
2Khushmeen Brar, Mtech Research Scholar, Department of Electronics and Communication Engineering, Chandigarh Engineering College (Mohali), India.
3Piyush Samant, Chandigarh University, Ghauran (Mohali), India.
Manuscript received on 09 August 2019 | Revised Manuscript received on 17 August 2019 | Manuscript Published on 26 August 2019 | PP: 10-17 | Volume-8 Issue-9S August 2019 | Retrieval Number: I10020789S19/19©BEIESP DOI: 10.35940/ijitee.I1002.0789S19
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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: Skin cancer is being classified among the mortal and exaggerating forms of cancer since decade. Notwithstanding, the early diagnosis of skin cancer is very important and it is an extravagant procedure. The presence of human skin is tough to examine and to model. This is because of its complex surface. The difficulty of the irregular edge, tone, appearance of thick hair and other alleviating features generate the skin tough to be analyzed. Human skin has some non-identical sorts of textures that diseased skin can characterize between the textures of the healthy one. In consequence, considerable achievements have not been brought into the evolution of diagnosis approach for skin cancer. On the other hand, skin cancer diagnosis in dermoscopic portrayals is an exceptionally troublesome chore. Notwithstanding, the early diagnosis of skin cancer is very important and it is an extravagant procedure. In consequence, considerable achievements have not been brought into the evolution of diagnosis approach for skin cancer. For precise diagnosis and categorization of skin cancer, particular features are recommended that one may categorize benign and malignant illustration. A midst analysts, governing the efficacious mechanism of skin cancer diagnosis is an all-important matter of contention. Ascertaining the more proficient methods of diagnosis to minimize the amount of errors is a crucial subject among analysts. Image processing is used to recognize the affected area by disease, its form and color etc. The reason behind it is to minimize the percentage of miscalculations. Computer vision can bring about influential contribution in skin cancer detection. Computer Aided Diagnosis provides support in the premature diagnosis of skin cancer. Programmed skin abrasion segregation is a stimulating chore because of the low contrast between abrasion and encompassing skin and asymmetrical periphery. In this paper we present an amalgamation of statistical features, GLCM features and GLRLM features. The recommended methodology is enacted on 100 images from PH2 database. In this work 2 segmentation tactics namely Otsu’s thresholding and Region growing have been explored. The performance of two systems is measured in terms of sensitivity, specificity, accuracy, precision, recall and f-measure.
Keywords: Biomedical Image Processing, Skin Cancer, Gray-Level Co-Occurrence Matrix, Grey-Level Run Length Matrix, Region Growing, Otsu Thresholding, Supervised Classification.
Scope of the Article: Biomedical Computing