A DE-ANN Inspired Skin Cancer Detection Approach Using Fuzzy C-Means Clustering

• Published in: Mobile networks and applications Vol. 25; no. 4; pp. 1319 - 1329
• Main Authors: Kumar, Manoj, Alshehri, Mohammed, AlGhamdi, Rayed, Sharma, Purushottam, Deep, Vikas
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.08.2020; Springer Nature B.V
• Subjects: Accuracy; Artificial neural networks; Cancer; Clustering; Communications Engineering; Computer Communication Networks; Computer simulation; Electrical Engineering; Electronic devices; Engineering; Evolutionary algorithms; Evolutionary computation; Image classification; Image enhancement; Image segmentation; IT in Business; Medical imaging; Medical science; Melanoma; Networks; Skin; Skin cancer; Image segmentation; Fuzzy clustering; Skin Cancer detection; DE-ANN; Melanoma
• ISSN: 1383-469X; 1572-8153
• DOI: 10.1007/s11036-020-01550-2

As per recent developments in medical science, the skin cancer is considered as one of the common type disease in human body. Although the presence of melanoma is viewed as a form of cancer, it is challenging to predict it. If melanoma or other skin diseases are identified in the early stages, prognosis can then be successfully achieved to cure them. For this, medical imaging science plays an essential role in detecting such types of skin lesions quickly and accurately. The application of our approaches is to improve skin cancer detection accuracy in medical imaging and further, can be automated using electronic devices such as mobile phones etc. In the proposed paper, an improved strategy to detect three type of skin cancers in early stages are suggested. The considered input is a skin lesion image which by using the proposed method, the system would classify it into cancerous or non-cancerous type of skin. The image segmentation is implemented using fuzzy C-means clustering to separate homogeneous image regions. The preprocessing is done using different filters to enhance the image attributes while the other features are assessed by implementing rgb color-space, Local Binary Pattern (LBP) and GLCM methods altogether. Further, for classification, artificial neural network (ANN) is trained using differential evolution (DE) algorithm. Various features are accurately estimated to achieve better results using skin cancer image datasets namely HAM10000 and PH2. The novelty of the work suggests that DE-ANN is best compared among other traditional classifiers in terms of detection accuracy as discussed in result section of this paper. The simulated result shows that the proposed technique effectually detects skin cancer and produces an accuracy of 97.4%. The results are highly accurate compare to other traditional approaches in the same domain.