Medical Image Analysis using Convolutional Neural Networks: A Review

Abstract

The science of solving clinical problems by analyzing images generated in clinical practice is known as medical image analysis. The aim is to extract information in an effective and efficient manner for improved clinical diagnosis. The recent advances in the field of biomedical engineering has made medical image analysis one of the top research and development area. One of the reason for this advancement is the application of machine learning techniques for the analysis of medical images. Deep learning is successfully used as a tool for machine learning, where a neural network is capable of automatically learning features. This is in contrast to those methods where traditionally hand crafted features are used. The selection and calculation of these features is a challenging task. Among deep learning techniques, deep convolutional networks are actively used for the purpose of medical image analysis. This include application areas such as segmentation, abnormality detection, disease classification, computer aided diagnosis and retrieval. In this study, a comprehensive review of the current state-of-the-art in medical image analysis using deep convolutional networks is presented. The challenges and potential of these techniques are also highlighted.

References in corpus (2)

• Domain-adversarial neural networks to address the appearance variability of histopathology images
• Deep Multi-scale Location-aware 3D Convolutional Neural Networks for Automated Detection of Lacunes of Presumed Vascular Origin

Cited by in corpus (23)

• MultiResUNet : Rethinking the U-Net Architecture for Multimodal Biomedical Image Segmentation
• Recent advances and clinical applications of deep learning in medical image analysis
• Segment Anything Model for Medical Image Analysis: an Experimental Study
• A Review on Deep Learning Techniques for the Diagnosis of Novel Coronavirus (COVID-19)
• Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems
• Classification of Arrhythmia by Using Deep Learning with 2-D ECG Spectral Image Representation
• Deep Learning in Cardiology
• MIScnn: A Framework for Medical Image Segmentation with Convolutional Neural Networks and Deep Learning
• Review: Deep Learning in Electron Microscopy
• BGnet: Accurate and rapid background estimation in single-molecule localization microscopy with deep neural nets
• A Survey of Deep Learning Techniques for the Analysis of COVID-19 and their usability for Detecting Omicron
• Artificial General Intelligence for Medical Imaging Analysis
• Bayesian neural networks via MCMC: a Python-based tutorial
• Early Disease Stage Characterization in Parkinson's Disease from Resting-state fMRI Data Using a Long Short-term Memory Network
• An Overview of Healthcare Data Analytics With Applications to the COVID-19 Pandemic
• Current and future roles of artificial intelligence in retinopathy of prematurity
• Improving Generalization Capability of Deep Learning-Based Nuclei Instance Segmentation by Non-deterministic Train Time and Deterministic Test Time Stain Normalization
• Semi-Supervised Deep Learning for Multi-Tissue Segmentation from Multi-Contrast MRI
• Deep Neural Networks for Detecting Statistical Model Misspecifications. The Case of Measurement Invariance
• Automated skin lesion segmentation using multi-scale feature extraction scheme and dual-attention mechanism
• Hyperuniformity of Weighted Particle Systems
• Inflation of test accuracy due to data leakage in deep learning-based classification of OCT images
• Unmasking Biases and Reliability Concerns in Convolutional Neural Networks Analysis of Cancer Pathology Images