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

Year: 2020

Type: preprint

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.

DOI: 10.21203/rs.3.rs-17004/v1

Institutions: Chungbuk National University, Egyptian Atomic Energy Authority, Menoufia University, Kafrelsheikh University, University of Liverpool, Minia University

Language: English

Topic: Anomaly Detection Techniques and Applications

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funders: Kafrelsheikh University, University of Liverpool, National Research Foundation, National Research Foundation of Korea, Iran Telecommunication Research Center, Minia University, Institute for Information and Communications Technology Promotion

Awards: G1902236933, G3741983864, G3991765419, G5567961701, G6545090072, G6927130708, G8602222919

OpenAlex ID: W3112142095