Deep learning-based techniques are efficient for early and accurate diagnosis of disease, helping healthcare practitioners to save lives.
Here’s what we’ll cover:
In the past few years, deep learning-based techniques have evolved and revolutionized many industries, including healthcare.
Deep learning-based techniques are efficient for early and accurate diagnosis of disease, helping healthcare practitioners save many lives. In this article, we’ll discuss medical imaging and the evolution of deep learning-based techniques.
Medical imaging comprises different techniques to create visual representations of internal parts of the human body, like tissues or organs, to monitor their functioning, diagnose, and treat diseases. Medical imaging also adds to anatomy and physiology databases.
Medical imaging techniques include radiography, MRI, ultrasound, endoscopy, thermography, tomography, and so on. Different techniques provide tailored information related to specific areas of the human body.
There are many players manufacturing medical imaging devices, which include Siemens Healthineers, Hitachi, GE, Fujifilm, Samsung, and Toshiba. There is a huge global market for medical imaging devices, which is expected to grow to a whopping $48.6 billion by 2025.
High-quality images provided by different medical imaging techniques can improve the decision-making process and avoid unnecessary medical procedures. These techniques can diagnose many diseases and injuries like cancer, pneumonia, brain injuries, internal bleeding, and so on.
The interpretation and understanding of medical images are limited because of different parameters, complexity, and requirement of core subject knowledge. In the past, people tried to use machine learning algorithms like logistic regression, decision trees, support vector machines, and so on, to understand medical images.
This approach has some limitations:
In contrast, deep learning-based algorithms capture hidden and subtle representations and automatically process raw data and extract features without requiring manual interventions. These benefits over traditional approaches lead to their fast adaptation in medical imaging, as mentioned in the next section.
Evolution started from AlexNet, the first neural net to win the ILSVRC image classification competition back in 2012. After that, many architectures came that include VGG Net, Inception (GoogleNet), ResNet, etc. Recently, Vit-H/14 and FixEfficientNet-L2 are in first and second positions respectively on ImageNet leaderboard according to Top-1 accuracy.
The use of pre-trained models for other applications using the fine-tuning technique opened endless possibilities without the need for training models from scratch. However, pre-trained models on medical images are hard to find and are an area of improvement.
Here are some areas where deep learning is applied:
Although the deep learning based-approach is suitable for medical imaging, there are some challenges we need to address:
Apart from these challenges, deep learning-based techniques are improving and, in some cases, increasing performance beyond subject experts. Currently, it is positioned as a great assistant to medical experts, rather than a replacement.
Check out Kaggle’s medical datasets and competitions to explore applications of machine learning in medical imaging.
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