Transfer Learning-Based Segmentation of Pneumonia from Chest X-Rays Images
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Abstract
Pneumonia remains a significant global health concern, warranting precise and efficient diagnostic tools. This study introduces a comprehensive approach to pneumonia segmentation leveraging advanced deep learning techniques. The primary goal is to enhance the precision of pneumonia localization within medical images, specifically chest X-rays, through the utilization of state-of-the-art deep learning models. This study explores the application of advanced segmentation models, namely DeepLabv3 and SegNet, for the automated identification and delineation of pneumonia-affected regions within chest X-ray images. DeepLabv3, renowned for its semantic segmentation capabilities, and SegNet, featuring an encoder-decoder architecture, are selected as the segmentation models. The training process of the system leverages the widely acknowledged Kermany dataset, specifically composed of chest X-ray images depicting cases of pneumonia. This dataset is well-established and holds a prominent status within the field, recognized for its relevance and significance in the context of pneumonia detection and classification tasks. As per the evaluation findings, it is evident that the system attains enhanced accuracy when employing the DeepLabv3 architecture compared to the SegNet architecture.
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