Enhancing chest image classification using PCA and CNN model

Abstract

Deep learning (DL) has a significant impact on X-ray images for diagnosing and categorizing a range of lung disorders. The proliferation of extensive annotated image datasets has resulted in the emergence of convolutional neural networks (CNNs) as a useful instrument for the tasks of image recognition and categorization. Despite this abundance, the primary challenge in medical diagnosis continues to be the classification of these images. This research aims to enhance image classifiers by using the CNN model; both training and testing datasets underwent analysis using the suggested CNN system. An analysis and comparison are conducted on the impact of feature extraction using the Principal Components Analysis (PCA) technique. The study attains maximal classification efficiency by preparing images through dimensionality reduction before classification, concurrently enhancing the efficacy of CNNs in feature extraction. The optimal tuning strategies for enhancing the performance of the proposed CNN were found to include boosting the number of epochs, changing the optimizer, and decreasing the learning rate, as well as improving algorithm gains by using pre-trained weights. The suggested system outperforms previously utilized approaches like VGG or DenseNet, with more than 99.80 percent accuracy, precision, recall, and F1-score values. The suggested methodology demonstrates considerable promise for enhancing the efficacy and precision of lung disease diagnoses derived from chest X-ray images, thereby offering clinicians beneficial decision support and accelerating the implementation of treatment strategies. Furthermore, the developed model facilitates the identification of pulmonary ailments, encompassing critical conditions like COVID-19, thereby facilitating timely and efficacious patient care.