2021
DOI: 10.48550/arxiv.2106.04919
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Cervical Cytology Classification Using PCA & GWO Enhanced Deep Features Selection

Hritam Basak,
Rohit Kundu,
Sukanta Chakraborty
et al.

Abstract: Cervical cancer is one of the most deadly and common diseases among women worldwide. It is completely curable if diagnosed in an early stage, but the tedious and costly detection procedure makes it unviable to conduct population-wise screening. Thus, to augment the effort of the clinicians, in this paper, we propose a fully automated framework that utilizes Deep Learning and feature selection using evolutionary optimization for cytology image classification. The proposed framework extracts Deep feature from se… Show more

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“…In this paper, we propose Reinforced Swarm Optimization (RSO), a novel optimization algorithm for feature selection that incorporates the features of both reinforcement learning along with swarm intelligence based BSO algorithm. BSO [5], is a metaheuristic optimization algorithm, that mimics the foraging activities of bee colony and have been used in various domains including cloud computing [18], maximum satisfiability problem (MAX-SAT) [19], document retrieval [20], parallel computing [21], biomedical image analysis [22], [23], and many more. On the other hand, reinforcement learning (RL) has been integrated into BSO to make it more adaptive and robust powered by a suitable balance between diversification and intensification of the search space, compensating the local search of the BSO search agents.…”
Section: Introductionmentioning
confidence: 99%
“…In this paper, we propose Reinforced Swarm Optimization (RSO), a novel optimization algorithm for feature selection that incorporates the features of both reinforcement learning along with swarm intelligence based BSO algorithm. BSO [5], is a metaheuristic optimization algorithm, that mimics the foraging activities of bee colony and have been used in various domains including cloud computing [18], maximum satisfiability problem (MAX-SAT) [19], document retrieval [20], parallel computing [21], biomedical image analysis [22], [23], and many more. On the other hand, reinforcement learning (RL) has been integrated into BSO to make it more adaptive and robust powered by a suitable balance between diversification and intensification of the search space, compensating the local search of the BSO search agents.…”
Section: Introductionmentioning
confidence: 99%