Integrated design of deep features fusion for localization and classification of skin cancer

The common fatal type of skin cancer is melanoma. Recently, numerous intelligent systems are used to detect skin cancer at an early stage. These systems are helpful for a dermatologist as a preliminary judgment to diagnose skin cancer. However, accurate skin lesion detection is an intricate task. This work comprises three main phases, firstly perform preprocessing to resize the images to 240 x 240 x 3 and convert RGB into L-boolean AND* a(boolean AND*) b(boolean AND*) in which the luminance channel is selected. Secondly, Biorthogonal 2-D wavelet transform, Otsu algorithm are used to segment the skin lesion. Thirdly, deep features extracted from pre-trained Alex net and VGG16 and serially fused. The applied PCA for optimal features selection for classification into benign and malignant. The publically available datasets (PH2, ISBI 2016- 2017) are merged to form a single large dataset for the validated of proposed method. The results comparison is performed with the existing work which confirms that the proposed method classifies the skin lesion more accurately. (C) 2019 Published by Elsevier B.V.