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Classification of medical images based on deep stacked patched auto-encoders

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Abstract

The concept of artificial intelligence is not new. Without going into details of the evolution of artificial intelligence, we can confess that recent techniques of deep neural networks have considerably relaunched the trend with a significant advance namely the ability to automatically learn high-level concepts. However, a great step has been taken in deep learning to help researchers perform segmentation, feature extraction, classification and detection from raw medical images. This paper concerns the automatic classification of medical images with deep neural networks. We aimed at developing a system for automatic classification of medical images and detection of anomalies in order to provide a decision-making tool for the doctor. In this component we proposed a method for classifying medical images based on deep neural network using sparse coding and wavelet analysis. Serval real databases are used to test the proposed methods: MIAS and DDSM for mammogram images, LIDC-IDRI for lung images and dental dataset images. Classifications rates given by our approach show a clear improvement compared to those cited in this article.

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Acknowledgment

The authors would like to acknowledge the financial support of this work by grants from General Direction of Scientific Research (DGRST), Tunisia, under the ARUB program.

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Authors and Affiliations

  1. Research Team in Intelligent Machines (RTIM), University of Gabes, ENIG, Zrig 6029, Gabes, Tunisia

    Ramzi Ben Ali & Ridha Ejbali

  2. RTIM, Gabes, Tunisia

    Mourad Zaied

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  1. Ramzi Ben Ali
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  2. Ridha Ejbali
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  3. Mourad Zaied
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Correspondence to Ramzi Ben Ali.

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Ben Ali, R., Ejbali, R. & Zaied, M. Classification of medical images based on deep stacked patched auto-encoders. Multimed Tools Appl 79, 25237–25257 (2020). https://doi.org/10.1007/s11042-020-09056-5

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