Medical Image Analysis using Convolutional Neural Networks: A Review

TL;DR

This paper reviews how deep convolutional neural networks are revolutionizing medical image analysis, improving diagnosis and disease detection through automated feature learning and various clinical applications.

Contribution

It provides a comprehensive overview of current deep learning techniques in medical image analysis and discusses their challenges and potential for future clinical use.

Findings

Deep learning enhances accuracy in medical image segmentation and classification.

Convolutional neural networks automate feature extraction, reducing manual effort.

Challenges include data scarcity and model interpretability.

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.