An image compression approach for efficient pneumonia recognition

ABSTRACT

Increasingly, analytics such as classification and detection suffer from a significant amount of generated visual data. Nonetheless, recent approaches have not given substantial thought to CAD systems within limited capacities at the expense of performance. For that purpose, we proposed an autoencoder-based classification approach for pneumonia recognition, extending the use of the features extracted by autoencoders for compression to enhance efficiency. Thus, we substitute the classification of images with compressed sequences and encoded tensors, representing a more convenient format for managing and storing data. Which significantly minimizes computing costs and enhances transmission bandwidth. We designed CNN model introducing the attention mechanism to the latent space with minimum parameters optimizing the classification complexity. We assess our method's effectiveness on two medical imaging datasets. In addition, we compared latent space classification to multi-resolution image classification performance. Our approach improves state-of-art performance, boosting efficiency; the number of parameters is negligible, reduced by 69%.

KEYWORDS:

• Medical image classification
• pneumonia recognition
• image compression
• auto-encoder
• recurrent neural network
• attention module
• Deep Learning
• CNN

Competing interests

The authors have no competing interests to declare that are relevant to the content of this article.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 https://www.kaggle.com/c/rsna-pneumonia-detection-challenge.

Additional information

Funding

The authors did not receive support from any organization for the submitted work.

Notes on contributors

Sabrina Nefoussi

Sabrina Nefoussi, Ph.D. student in Computer Science at Ecole Militaire Polytechnique (EMP). Her research interests are in the areas of Artificial Intelligence, Pattern Recognition, Medical Image Analysis, and Deep Learning Computer Vision.

Abdenour Amamra

Abdenour Amamra, Associate professor in Computer Engineering at Ecole Militaire Polytechnique (EMP). His research interests are in the areas of Image Processing, Computer Vision, Pattern Recognition, Machine Learning, Signal Processing, Feature Extraction, Digital Image Processing, Image Segmentation, Object Recognition, Machine Vision, Image Data Analysis, Object Tracking, Medical Engineering Image Analysis, and Classification.

Idir Amine Amarouche

Amarouche Idir Amine, Ph.D. has been working on main issues related to electronic healthcare data management. His research interests are in the areas of Artificial Intelligence, Databases, and Information Systems (Business Informatics).