Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Hybrid Feature Fusion and Machine Learning Approaches for Melanoma Skin Cancer Detection

Version 1 : Received: 16 January 2022 / Approved: 18 January 2022 / Online: 18 January 2022 (12:43:50 CET)

How to cite: Rahman, M.M.; Kamal Nasir, M.; A-Alam, N.; Islam Khan, S.; Band, S.; Dehzangi, I.; Beheshti, A.; Alinejad Rokny, H. Hybrid Feature Fusion and Machine Learning Approaches for Melanoma Skin Cancer Detection. Preprints 2022, 2022010258 (doi: 10.20944/preprints202201.0258.v1). Rahman, M.M.; Kamal Nasir, M.; A-Alam, N.; Islam Khan, S.; Band, S.; Dehzangi, I.; Beheshti, A.; Alinejad Rokny, H. Hybrid Feature Fusion and Machine Learning Approaches for Melanoma Skin Cancer Detection. Preprints 2022, 2022010258 (doi: 10.20944/preprints202201.0258.v1).

Abstract

Skin cancer is an exquisite disease globally nowadays. Because of the poor contrast and apparent resemblance between skin and lesions, automatic identification of skin cancer is complicated. The rate of human death can be massively reduced if melanoma skin cancer can be detected quickly using dermoscopy images. In this research, an anisotropic diffusion filtering method is used on dermoscopy images to remove multiplicative speckle noise and the fast-bounding box (FBB) method is applied to segment the skin cancer region. Furthermore, the paper consists of two feature extractor parts. One of the two features extractor parts is the hybrid feature extractor (HFE) part and another is the convolutional neural network VGG19 based CNN feature extractor part. The HFE portion combines three feature extraction approaches into a single fused feature vector: Histogram-Oriented Gradient (HOG), Local Binary Pattern (LBP), and Speed Up Robust Feature (SURF). The CNN method also is used to extract additional features from test and training datasets. This two-feature vector is fused to design the classification model. This classifier performs the classification of dermoscopy images whether it is melanoma or non-melanoma skin cancer. The proposed methodology is performed on two ordinary datasets and achieved the accuracy 99.85%, sensitivity 91.65%, and specificity 95.70%, which makes it more successful than previous machine learning algorithms.

Keywords

Skin cancer; Deep learning; Hybrid feature extractor; Local binary pattern; Feature extraction

Subject

MATHEMATICS & COMPUTER SCIENCE, Artificial Intelligence & Robotics

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