Sine Cosine Based Harris Hawks Optimizer: A Hybrid Optimization Algorithm for Skin Cancer Detection Using Deep Stack Auto Encoder

• Published in: Revue d intelligence artificielle Vol. 36; no. 5; p. 697
• Main Authors: Madiwal, Shweta M, Burkpalli, Vishwanath
• Format: Journal Article
• Language: English
• Published: Edmonton International Information and Engineering Technology Association (IIETA) 01.10.2022
• Subjects: Algorithms; Cancer; Classification; Coders; Discriminant analysis; Entropy; Inspection; Kurtosis; Mortality; Neural networks; Optimization; Skin cancer; Trigonometric functions
• ISSN: 0992-499X; 1958-5748; 1958-5748
• DOI: 10.18280/ria.360506

Skin cancer is becoming major problems due to its tremendous growth. Skin cancer is a malignant skin lesion, which may cause damage to human. Hence, prior detection and precise medical diagnosis of the skin lesion is essential. In medical practice, detection of malignant lesions needs pathological examination and biopsy, which is expensive. The existing techniques need a brief physical inspection, which is imprecise and time-consuming. This paper presents a computer-assisted skin cancer detection strategy for detecting the skin lesion in skin images using deep stacked auto encoder. Sine Cosine-based Harris Hawks Optimizer (SCHHO) trains deep stacked auto encoders. The proposed SCHHO algorithm is designed by combining Sine Cosine Algorithm (SCA) and Harris Hawks Optimizer (HHO). The identification of skin lesion is performed on each segment, which is obtained by sparse-Fuzzy-c-means (FCM) algorithm. Statistical features, texture features and entropy are employed for selecting the most significant feature. Mean, standard deviation, variance, kurtosis, entropy, and Linear Discriminant Analysis (LDP) featured are extracted. SCHHO-Deep stacked auto-encoder outperformed other approaches with 91.66% accuracy, 91.60% sensitivity, and 91.72% specificity.