Abstract

Medical image classification plays a pivotal role in diagnosing various diseases. However, selecting informative features from these images remains a challenging task due to the high dimensionality and complexity of the data. Genetic algorithms (GAs) offer a promising approach for feature selection in medical image classification tasks by mimicking the process of natural selection to evolve optimal solutions. This study proposes a genetic algorithm optimization framework for feature selection in medical image classification. The GA iteratively searches the feature space to find the subset of features that maximizes the classification performance. Fitness evaluation is based on a classifier’s performance using selected features, and genetic operators such as crossover and mutation are applied to produce new generations of feature subsets. The proposed framework contributes to enhancing the efficiency and effectiveness of medical image classification by identifying relevant features. By employing GAs, it overcomes the limitations of traditional feature selection methods and adapts to the complexity of medical image data. Experimental results on benchmark medical image datasets demonstrate the effectiveness of the proposed approach. Significant improvements in classification accuracy and computational efficiency are observed compared to baseline methods. Moreover, the selected features exhibit robustness across different classifiers, highlighting the generalizability of the proposed framework.