Hybrid Genetic Algorithm and Deep Learning Approach for Lung Nodule Detection and Classification in Chest X-rays
Keywords:
Classification, Deep Learning, Genetic Algorithm, Lung Nodule detectionAbstract
Early diagnosis and treatment of lung disorders rely heavily on the detection and classification of lung nodules in chest X-rays. In this research, we offer a unique method for improving the accuracy and efficiency of lung nodule identification and classification by combining the power of genetic algorithms (GAs) with deep learning approaches.To begin, we preprocess chest X-ray pictures to improve their quality and reduce noise, providing the best possible input for the subsequent analysis that follows. Then, as the core of our deep learning model, we use a convolutional neural network (CNN). This convolutional neural network (CNN) is taught to recognise lung nodules based on a large dataset of chest X-ray pictures.We introduce a genetic algorithm-based optimisation technique to further enhance the deep learning model's performance. By simulating natural selection and evolution, genetic algorithms let us optimise the CNN model's hyperparameters. Step-by-step guide on making your X-ray images more robust and your X-ray detector more accurate.Our method's categorization stage entails labelling any discovered nodules as either benign or malignant. Since our deep learning model was trained to recognise important features in X-ray pictures, it performs admirably in this application. We take advantage of the genetic algorithm's search capabilities to fine-tune the model's classification parameters, resulting in improved classification accuracy, by merging genetic algorithms with deep learning.
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