Development of a deep learning model to automatically localize and classify thoracic abnormalities from chest radiographs.
© Copyright 2022, All rights reserved to Hans Haller, CSTE-CIDA Student at Cranfield Uni. SATM, Cranfield, UK.
Chest radiographs are a common medical imaging modality that can reveal critical information about the health of the lungs, heart, and other thoracic structures. However, interpreting these images can be challenging, especially for abnormalities that are subtle or rare. As a result, machine learning and deep learning algorithms have emerged as powerful tools to assist radiologists in detecting and diagnosing these abnormalities.
In the context on this assignment, the usage of machine learning and deep learning algorithms are a great choice as they allow to automatically localize and classify thoracic abnormalities from chest radiographs, resulting in an improvement of the accuracy and efficiency of radiographic diagnosis, ultimately leading to improved patient outcomes and reduced healthcare costs.
Overall, the use of machine learning and deep learning algorithms in chest radiography is a relatively new and rapidly evolving field, representing the state-of-the-art of our time. As these algorithms continue to improve, they have the potential to revolutionize the field of radiology and improve the accuracy and efficiency of medical imaging diagnoses.
The results of the model are shown below:
The original images of the data that Kaggle made available were in dicom format, and the average size of each image was 3000x3000. As I explained, in order for the training to be possible by the time of the assignment submission, the images had to be converted in a .png format and reduced to 256x256. However, I did not have enough space on my laptop to download, extract and convert the images. Thus I tried to convert them directly from a Kaggle notebook using fairly simple code lines.