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Anomaly Detection from Medical Signals and Images Using Advanced Convolutional Neural Network

Article scientifique 2020 Anglais

Résumé

Abstract In the field of Artificial Intelligence (AI), deep learning is a method falls in the wider family of machine learning algorithms that works on the principle of learning. Convolutional Neural Networks (CNNs) can be used for pattern recognition from different images based on deep learning. Anomaly detection is a very vital area in medical signal and image processing due to its importance in automatic diagnosis. Anomaly detection from medical EEG signals based on spectrogram and medical corneal images are tested and evaluated in this paper. Technically, deep learning CNN models are used in the train and test processes, each input image will pass through a series of convolution layers with filters (Kernels), pooling, and fully connected layers (FC) for the classification purposes. The presented simulation results reveal the success of the proposed techniques towards automated medical diagnosis.

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Abbass, M., Kwon, K., Kim, N., Abdelwahab, S., Haggag, N., Ibrahim, F., Mahrous, Y., Seddik, A., Khalil, A., Elsherbeeny, Z., El‐Shafai, W., Rihan, M., El‐Banby, G., Soltan, E., Soliman, N., Algarni, A., Al‐Hanafy, W., El‐Fishawy, A., El‐Rabaie, E., Al‐Nuaimy, W., Dessouky, M., Saleeb, A., Messiha, N., El‐Dokany, I., El‐Samie, F., Khalaf, A. (2020). Anomaly Detection from Medical Signals and Images Using Advanced Convolutional Neural Network. https://doi.org/10.21203/rs.3.rs-17004/v1

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Auteur(s)

Mohammed Y. Abbass Ki‐Chul Kwon Nam Kim Safey A. Abdelwahab Nehad Haggag Fatma E. Ibrahim Yasser Mahrous Ahmad Seddik Ali Khalil Zeinab Elsherbeeny Walid El‐Shafai Mohamad Rihan Ghada M. El‐Banby Eman Soltan Naglaa F. Soliman Abeer D. Algarni Waleed Al‐Hanafy Adel S. El‐Fishawy El‐Sayed M. El‐Rabaie Waleed Al‐Nuaimy Moawad I. Dessouky Adel Saleeb Nagy W. Messiha Ibrahim M. El‐Dokany Fathi E. Abd El‐Samie Ashraf A. M. Khalaf

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