Multi Class Classification of Lung Disease Through Customized VGG-19 From Chest X-Rays
Keywords:
Covid, Normal, Pneumonia, Tuberculosis, Deep Learning, VGG19, Transfer LearningAbstract
Several global epidemic lung diseases, including COVID-19, tuberculosis (TB), and pneumonia, have increased in large numbers, resulting in the loss of millions of lives. Identifying these diseases accurately poses a challenge for medical specialists, primarily due to minute differences in Chest X-Ray images (CXR). This study proposes a computer-aided method for identifying lung diseases based on CXR images to support healthcare professionals. CXR is a widely used diagnostic tool in the healthcare sector, providing both rapid and precise diagnoses. Algorithms like deep learning have demonstrated exceptional capabilities in detecting and classifying lung diseases, streamlining the diagnostic process and saving valuable time for medical practitioners. This research introduces a customized VGG-19developed architecture for multiclass classification of Covid, Normal, Pneumonia, and tuberculosis (TB). A total of 5928 CXR images, sourced from various open-access websites (Covid 1626, Normal 1802, Pneumonia 1800, tuberculosis 700) were used, various pre-processing techniques like resizing, Gaussian filter for noise removal and CLACHE for image enhancement is used and data augmentation for increasing the size of dataset. Based on experimental data, our suggested model performed good with an accuracy of 93.33%.
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