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. 2023 Sep 18;13(18):2979.
doi: 10.3390/diagnostics13182979.

Using Computer Vision Techniques to Automatically Detect Abnormalities in Chest X-rays

Zaid Mustafa  1 , Heba Nsour  2
Affiliations

Using Computer Vision Techniques to Automatically Detect Abnormalities in Chest X-rays

Zaid Mustafa et al. Diagnostics (Basel). .

Abstract

Our research focused on creating an advanced machine-learning algorithm that accurately detects anomalies in chest X-ray images to provide healthcare professionals with a reliable tool for diagnosing various lung conditions. To achieve this, we analysed a vast collection of X-ray images and utilised sophisticated visual analysis techniques; such as deep learning (DL) algorithms, object recognition, and categorisation models. To create our model, we used a large training dataset of chest X-rays, which provided valuable information for visualising and categorising abnormalities. We also utilised various data augmentation methods; such as scaling, rotation, and imitation; to increase the diversity of images used for training. We adopted the widely used You Only Look Once (YOLO) v8 algorithm, an object recognition paradigm that has demonstrated positive outcomes in computer vision applications, and modified it to classify X-ray images into distinct categories; such as respiratory infections, tuberculosis (TB), and lung nodules. It was particularly effective in identifying unique and crucial outcomes that may, otherwise, be difficult to detect using traditional diagnostic methods. Our findings demonstrate that healthcare practitioners can reliably use machine learning (ML) algorithms to diagnose various lung disorders with greater accuracy and efficiency.

Keywords: CAD; abnormalities; computer vision techniques; deep learning algorithm; image classification; image processing; image techniques; machine learning; magnetic resonance imaging; object detection; pneumonia.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Training and tuning the DL system: Refining and enhancing the DL system via iterative training and parameter adjustment to improve performance and accuracy [13]. (A) Training and tuning; (B) Operating points selection; (C) Deep learning system (DLS) and radiologists evaluation [10].
Figure 2
Figure 2
The different types of lung infections. Eight of the most common lung diseases; namely, infiltration, atelectasis, cardiac hypertrophy, effusion, lumps, nodules, pneumonia, and pneumothorax; observed in the chest radiographs [12].
Figure 3
Figure 3
The different types of lung infections and the percentages of patients with each type of infection [10]. As seen, pneumonia is the most prevalent followed by bronchitis and TB [4].
See this image and copyright information in PMC

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