Abstract
Artificial intelligence (AI) has played a significant role in image analysis and feature extraction, applied to detect and diagnose a wide range of chest-related diseases. Although several researchers have used current state-of-the-art approaches and have produced impressive chest-related clinical outcomes, specific techniques may not contribute many advantages if one type of disease is detected without the rest being identified. Those who tried to identify multiple chest-related diseases were ineffective due to insufficient data and the available data not being balanced. This research provides a significant contribution to the healthcare industry and the research community by proposing a synthetic data augmentation in three deep Convolutional Neural Networks (CNNs) architectures for the detection of 14 chest-related diseases. The employed models are DenseNet121, InceptionResNetV2, and ResNet152V2; after training and validation, an average ROC-AUC score of 0.80 was obtained competitive as compared to the previous models that were trained for multi-class classification to detect anomalies in x-ray images. This research illustrates how the proposed model practices state-of-the-art deep neural networks to classify 14 chest-related diseases with better accuracy.
Highlights
Medical X-rays, short for X-radiation, are a way to look to visible light in classical physics but with higher energy hits the body (Panwar et al, 2020)
We investigate three deep convolutional neural network models (DenseNet121, InceptionResNetV2, and ResNet152V2) to design the predictive model
The data was distributed as 80% and 20% for training and testing, respectively
Summary
Medical X-rays, short for X-radiation, are a way to look to visible light in classical physics but with higher energy hits the body (Panwar et al, 2020). X-ray is employed to generate images of tissues and structures inside the body; these include bones, chest, teeth, and so on Rajaraman & Antani (2020). X-rays are handy diagnostic tools used for several decades by specialists to detect fractures, certain tumors, pneumonia, dental problems, and others. CT (Computed Tomography) can produce a series of body images that are later assembled into a three-dimensional X-ray image processed by computer. The standard X-ray is faster, easier, cheaper, and less harmful than the CT scan (Rajaraman & Antani, 2020). AI-driven deep CNN approach for multi-label pathology classification using chest X-Rays.
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