Hybrid Feature Selection Model for Computer Aided Diagnosis System in Lung Segmentation
Keywords:
CAD, Single objective feature selection algorithm, PSOAAbstract
The difficult lung disorder classification module in computer-aided diagnosis (CAD) is called feature selection. This is mostly caused by the growing quantity of features that must be accurately and thoroughly examined. When the input dataset or feature sets are large, classification becomes a very time-consuming operation. In order to improve the efficiency of the classification subsystem, feature selection often involves choosing the most appropriate, practical features and minimizing redundancy. Therefore, choosing the best features can effectively boost any CAD system's accuracy, decrease its time complexity, and enhance its performance. This study presents and evaluates a hybrid feature selection technique that combines Tabu Search with the multi-objective Particle Swarm Optimization algorithm(PSOA). Only a single bag of the ideal solution was offered by the single-objective feature selection algorithms. By generating a series of optimal solutions that trade many objectives against one another, this method overcame the drawback of the conventional single-objective algorithm. The use of multiple objectives made sure that the fewest features with the greatest impact on classification were chosen, and it enhanced accuracy while reducing error rates for the input dataset under consideration. Using a multi-objective feature with a local Tabu search allowed for the selection of fewer features while also reducing the amount of errors that occurred. After that, it was validated by being compared to well-known feature optimization methods such as PSO and Bee Colony to confirm that it was accurate. As a result, the proposed algorithm for feature selection resulted in improvements in terms of accuracy, specificity, sensitivity, recall, and error rate, as demonstrated by the numerical results.
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