Efficient Anomaly Detection from Medical Signals and Images with CNNs for IoMT Systems

2021 ◽  
Author(s):  
Ali A. Khalil ◽  
Fatma Ibrahim ◽  
Mohamed Y. Abbass ◽  
Nehad Haggag ◽  
Yasser Mahrous ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Medical Signals

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

2020 ◽  
Author(s):  
Mohammed Abbass ◽  
Ki-Chul Kwon ◽  
Nam Kim ◽  
Safey A. Abdelwahab ◽  
Nehad Haggag ◽  
...  
Keyword(s):  
Deep Learning ◽  
Anomaly Detection ◽  
Input Image ◽  
Machine Learning Algorithms ◽  
Eeg Signals ◽  
Simulation Results ◽  
Pass Through ◽  
Fully Connected ◽  
Medical Signals ◽  
Medical Signal

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.

Efficient anomaly detection from medical signals and images

2019 ◽  
Vol 22 (3) ◽  
pp. 739-767
Author(s):  
Ahmed Sedik ◽  
Heba M. Emara ◽  
Asmaa Hamad ◽  
Eman M. Shahin ◽  
Noha A. El-Hag ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Medical Signals

Anomaly Detection in Sensor Data

2018 ◽  
Vol 18 (1) ◽  
pp. 20-32 ◽  
Author(s):  
Jong-Min Kim ◽  
Jaiwook Baik
Keyword(s):  
Anomaly Detection ◽  
Sensor Data

An Anomaly Detection Method with Exemplar Subsequence for Time Series Data

2016 ◽  
Vol 136 (3) ◽  
pp. 363-372
Author(s):  
Takaaki Nakamura ◽  
Makoto Imamura ◽  
Masashi Tatedoko ◽  
Norio Hirai
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Detection Method ◽  
Series Data

Proposal of the Anomaly Detection Method of Live-alone by LOF using Multiple Sensors

2015 ◽  
Vol 135 (12) ◽  
pp. 749-755
Author(s):  
Taiyo Matsumura ◽  
Ippei Kamihira ◽  
Katsuma Ito ◽  
Takashi Ono
Keyword(s):  
Anomaly Detection ◽  
Detection Method ◽  
Multiple Sensors
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