LeDNet: Localization-enabled Deep Neural Network for Multi-Label Radiography Image Classification

• URL: http://arxiv.org/abs/2407.03931v1
• Date: Thu, 4 Jul 2024 13:46:30 GMT
• Title: LeDNet: Localization-enabled Deep Neural Network for Multi-Label Radiography Image Classification
• Authors: Lalit Pant, Shubham Arora,
• Abstract summary: Multi-label radiography image classification has long been a topic of interest in neural networks research. We will use the chest x-ray images to detect thoracic diseases for this purpose. We propose a combination of localization and deep learning algorithms called LeDNet to predict thoracic diseases with higher accuracy.
• Score: 0.1227734309612871
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multi-label radiography image classification has long been a topic of interest in neural networks research. In this paper, we intend to classify such images using convolution neural networks with novel localization techniques. We will use the chest x-ray images to detect thoracic diseases for this purpose. For accurate diagnosis, it is crucial to train the network with good quality images. But many chest X-ray images have irrelevant external objects like distractions created by faulty scans, electronic devices scanned next to lung region, scans inadvertently capturing bodily air etc. To address these, we propose a combination of localization and deep learning algorithms called LeDNet to predict thoracic diseases with higher accuracy. We identify and extract the lung region masks from chest x-ray images through localization. These masks are superimposed on the original X-ray images to create the mask overlay images. DenseNet-121 classification models are then used for feature selection to retrieve features of the entire chest X-ray images and the localized mask overlay images. These features are then used to predict disease classification. Our experiments involve comparing classification results obtained with original CheXpert images and mask overlay images. The comparison is demonstrated through accuracy and loss curve analyses.

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