An Approach for Thoracic Syndrome Classification with Convolutional Neural Networks

• Published in: Computational and mathematical methods in medicine 2021-09, Vol.2021, p.3900254-10
• Main Authors: Juneja, Sapna, Juneja, Abhinav, Dhiman, Gaurav, Behl, Sanchit, Kautish, Sandeep
• Format: Article
• Language: English
• Subjects: Computational Biology; Databases, Factual; Humans; Machine Learning; Neural Networks, Computer; Radiographic Image Interpretation, Computer-Assisted - methods; Radiographic Image Interpretation, Computer-Assisted - statistics & numerical data; Radiography, Thoracic - statistics & numerical data; Stochastic Processes; Syndrome; Thoracic Diseases - classification; Thoracic Diseases - diagnostic imaging
• ISSN: 1748-670X; 1748-6718
• DOI: 10.1155/2021/3900254

There have been remarkable changes in our lives and the way we perceive the world with advances in computing technology. Healthcare sector is evolving with the intervention of the latest computer-driven technology and has made a remarkable change in the diagnosis and treatment of various diseases. Due to many governing factors including air pollution, there is a rapid rise in chest-related diseases and the number of such patients is rising at an alarming rate. In this research work, we have employed machine learning approach for the detecting various chest-related problems using convolutional neural networks (CNN) on an open dataset of chest X-rays. The method has an edge over the traditional approaches for image segmentation including thresholding, k-means clustering, and edge detection. The CNN cannot scan and process the whole image at an instant; it needs to recursively scan small pixel spots until it has scanned the whole image. Spatial transformation layers and VGG19 have been used for the purpose of feature extraction, and ReLU activation function has been employed due to its inherent low complexity and high computation efficiency; finally, stochastic gradient descent has been used as an optimizer. The main advantage of the current method is that it retains the essential features of the image for prediction along with incorporating a considerable dimensional reduction. The model delivered substantial improvement over existing research in terms of precision, f-score, and accuracy of prediction. This model if used precisely can be very effective for healthcare practitioners in determining the thoracic or pneumonic symptoms in the patient at an early stage thus guiding the practitioner to start the treatment immediately leading to fast improvement in the health status of the patient.