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

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.

Advancements in medical diagnosis and treatment through machine learning: A review

“…Large amounts of medical imagery have been analysed using algorithms of ML to seek out patterns that could indicate disease (Alloqmani et al, 2021; Patel et al, 2022). For instance, algorithms using ML have been applied to monitor chest X‐rays and to check inflammatory areas or fluid accumulation in the lobes of the lungs when diagnosing pneumonia (Abbass et al, 2020; Choi et al, 2019).…”

“…Bayesian networks are probabilistic graphical models that can be used to model the interactions between variables in a system. Bayesian networks can be used in genomics to examine gene–gene interactions and identify potential genetic pathways involved in the onset of disease (Abbass et al, 2020). Bayesian networks are utilized to construct a network of nodes and edges where each node represents a gene or other biological variables, and each edge denotes a conditional dependency between two nodes.…”

Deep Learning based Anomaly Detection in Images: Insights, Challenges and Recommendations

“…In the field of medical image processing, automated diagnosis is the primary and most important task. Automated diagnosis is based on the detection of abnormal behavior in the images [11]. Still detect abnormalities such as malignant tumors from medical images, including mammograms or CT scan, are ongoing research problems that attract a lot of attention with applications in medical diagnosis [9].…”