Swarm intelligence based clustering technique for automated lesion detection and diagnosis of psoriasis.

Background: In psoriasis skin disease, psoriatic cells develop rapidly than the normal healthy cells. This speedy growth causes accumulation of dead skin cells on the skin's surface, resulting in thick patches of red, dry, and itchy skin. This patches or psoriatic skin legions may exhibit similar characteristics as healthy skin, which makes lesion detection more challenging. However, for accurate disease diagnosis and severity detection, lesion segmentation has prime importance. In that context, our group had previously performed psoriasis lesion segmentation using the conventional clustering algorithm. However, it suffers from the constraint of falling into the local sub-optimal centroids of the clusters.

Objective: The main objective of this paper is to implement an optimal lesion segmentation technique with aims at global convergence by reducing the probability of trapping into the local optima. This has been achieved by integrating swarm intelligence based algorithms with conventional K-means and Fuzzy C-means (FCMs) clustering algorithms.

Methodology: There are a total of eight different suitable combinations of conventional clustering (i.e., K-means and Fuzzy C-means (FCMs)) and four swarm intelligence (SI) techniques (i.e., seeker optimization (SO), artificial bee colony (ABC), ant colony optimization (ACO) and particle swarm optimization (PSO)) have been implemented in this study. The experiments are performed on the dataset of 780 psoriasis images from 74 patients collected at Psoriasis Clinic and Research Centre, Psoriatreat, Pune, Maharashtra, India. In this study, we are employing swarm intelligence optimization techniques in combination with the conventional clustering algorithms to increase the probability of convergence to the optimal global solution and hence improved clustering and detection.

Results: The performance has been quantified in terms of four indices, namely accuracy (A), sensitivity (SN), specificity (SP), and Jaccard index (JI). Among the eight different combinations of clustering and optimization techniques considered in this study, FCM + SO outperformed with mean JI = 0.83, mean A = 90.89, mean SN = 92.84, and mean SP = 88.27. FCM + SO found statistical significant than other approaches with 96.67 % of the reliability index.

Conclusion: The results obtained reflect the superiority of the proposed techniques over conventional clustering techniques. Hence our research development will lead to an objective analysis for automatic, accurate, and quick diagnosis of psoriasis.