Intelligent skin cancer detection using enhanced particle swarm optimization

Abstract

In this research, we undertake intelligent skin cancer diagnosis based on dermoscopic images using a variant of the Particle Swarm Optimization (PSO) algorithm for feature optimization. Since the identification of the most significant discriminative characteristics of the benign and malignant skin lesions plays an important role in robust skin cancer detection, the proposed PSO algorithm is employed for feature optimization. It incorporates not only subswarms, local and global food and enemy signals, attraction and flee operations, and mutation-based local exploitation, but also diverse matrix representations to mitigate premature convergence of the original PSO algorithm. Specifically, two remote swarm leaders, which show similar fitness but low position proximity, are used to lead the subswarm-based search and to enable the exploration of more distinctive search regions. Modified velocity updating strategies are also proposed to enable the particles to follow multiple swarm leaders and avoid the local and global worst individuals, partially (i.e. in randomly selected sub-dimensions) and fully (in every dimension), with an attempt to search for global optima. Probability distribution and dynamic matrix representations are used to diversify the search process. Evaluated with multiple skin lesion and UCI databases and diverse unimodal and multimodal benchmark functions, the proposed PSO variant shows a superior performance over those of other advanced and classical search methods for identifying discriminative features that facilitate benign and malignant lesion classification as well as for solving diverse optimization problems with different landscapes. The Wilcoxon rank sum test is adopted to further ascertain superiority of the proposed algorithm over other methods statistically.

Distant Pedestrian Detection in the Wild using Single Shot Detector with Deep Convolutional Generative Adversarial Networks

“…The resulting models will also be deployed to other computer vision and transfer learning tasks such as image description generation [24], semantic image segmentation from dermoscopic and microscopic images [25,26], and facial and gesture expression recognition in the wild [27].…”

Binary Competitive Swarm Optimizer Approaches for Feature Selection

“…Input parameter: N, T max and φ (1) Initialize a population of particles, x (2) Calculate the fitness of particles, F(x) (3) Define the best particle as gbest (4) for t = 1 to maximum number of iterations, T max // Competition Strategy // (5) for i = 1 to half of population, N/ 2 (6) Random select two particles, x k and x m (7) if F(x k ) better than F(x m ) (8) x w = x k , x l = x m (9) else (10) x w = x m , x l = x k (11) end if (12) Add x w into new population (13) Remove x k and x m from the population (14) next i //Velocity and Position Update // (15) for i = 1 to half of population, N/ 2 (16) for d = 1 to the dimension of search space, D (17) Update velocity of loser using Equation (1) (18) Update position of loser as shown in Equation (2) (19) next d (20) Calculate the fitness of new loser, F(x l ) (21) Move new loser into new population (22) Update gbest if there is better solution (23) next i (24) Pass new population to next iteration (25) next t Output: Global best solution…”

“…end if (12) Add x w into new population (13) Remove x k and x m from the population (14) next i //Velocity and Position Update // (15) for i = 1 to half of population, N/ 2 (16) for d = 1 to the dimension of search space, D (17) Update velocity of loser using Equation (1) (18) Convert velocity into probability using S-shaped or V-shaped transfer function (19) Update position of loser as shown in Equation 7or Equation (12) (20) next d (21) Calculate the fitness of new loser, F(x l ) (22) Move new loser into new population (23) Update gbest if there is better solution (24) next i (25) Pass new population to next iteration (26) next t Output: Global best solution…”

Highly Accurate Melanoma Detection from Skin Images using Multiple Feature Extraction and Support Vector Machine (SVM)

“…This procedure employments advanced picture handling method and SVM for grouping. [6] This procedure has enlivened the early identification of skin cancers and requires no oil to be applied to your skin to accomplish clear sharp pictures of your moles. Along these lines, it's speedier also, cleaner approach.…”