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.