Handbook of Deep Learning in Biomedical Engineering and Health Informatics

Recent technological advancements in biomedical deep learning have enabled the development of more accurate and efficient diagnostic tools, as well as new treatments for diseases. Techniques such as convolutional neural networks, recurrent neural networks, and generative adversarial networks are used in these systems to analyze medical images, text data, and other biomedical data. Case studies include cancer detection, drug discovery, and patient diagnosis.

Format: Hardback
Length: 318 pages
Publication date: 22 September 2021
Publisher: Apple Academic Press Inc.

The field of biomedical deep learning has experienced significant advancements in recent years, leading to groundbreaking discoveries and innovations. These advancements have been driven by the increasing availability of large datasets, advanced computing power, and innovative algorithms. In this article, we will explore some of the most important recent technological advancements in the field.

Techniques:

Deep Learning: Deep learning is a subset of machine learning that involves training neural networks with multiple layers to perform complex tasks. It has been widely used in biomedical deep learning systems due to its ability to learn from large amounts of data and extract meaningful patterns.

Convolutional Neural Networks (CNNs): CNNs are a type of neural network that is particularly well-suited for image and video processing. They have been used in biomedical deep learning systems to analyze medical images, such as X-rays, CT scans, and MRI images, and to detect and classify diseases.

Recurrent Neural Networks (RNNs): RNNs are a type of neural network that is capable of remembering and processing sequences of data. They have been used in biomedical deep learning systems to analyze speech and text data, such as patient records and medical journals, and to predict disease outcomes.

Generative Adversarial Networks (GANs): GANs are a type of neural network that consists of two sub-networks, a generator and a discriminator. The generator creates new data that resembles the real data, while the discriminator tries to distinguish between real and fake data. GANs have been used in biomedical deep learning systems to generate realistic medical images and to analyze genetic data.

Platforms:

TensorFlow: TensorFlow is an open-source software library developed by Google for machine learning and deep learning. It is widely used in biomedical deep learning systems due to its simplicity, scalability, and flexibility.

PyTorch: PyTorch is an open-source software library developed by Facebook for machine learning and deep learning. It is similar to TensorFlow but has a more intuitive interface and is easier to use for beginners.

Keras: Keras is an open-source software library developed by Google for deep learning. It is a high-level API that allows developers to build and train neural networks with ease.

Tools:

OpenCV: OpenCV is an open-source computer vision library that provides a wide range of tools for image and video processing. It has been widely used in biomedical deep learning systems to analyze medical images and to develop computer vision applications.

NumPy: NumPy is a Python library that provides support for large, multi-dimensional arrays and matrices. It has been widely used in biomedical deep learning systems to manipulate and analyze large datasets.

Matplotlib: Matplotlib is a Python library that provides a wide range of tools for creating visualizations and plots. It has been widely used in biomedical deep learning systems to visualize data and to develop machine learning models.

Case Studies:

Breast Cancer Detection: Breast cancer is one of the most common cancers in women worldwide. In recent years, biomedical deep learning systems have been developed to detect breast cancer using medical images. One of the most successful examples is the Deep Learning for Breast Cancer Detection (DLBCD) project, which uses convolutional neural networks to analyze breast images and classify them as benign or malignant.

Heart Disease Detection: Heart disease is one of the leading causes of death worldwide. In recent years, biomedical deep learning systems have been developed to detect heart disease using medical images, such as echocardiograms and CT scans. One of the most successful examples is the Heart Disease Detection using Deep Convolutional Neural Networks (HDD-CNN) project, which uses convolutional neural networks to analyze heart images and classify them as normal or abnormal.

Diabetes Detection: Diabetes is a chronic disease that affects millions of people worldwide. In recent years, biomedical deep learning systems have been developed to detect diabetes using medical images, such as retinal images. One of the most successful examples is the Diabetes Detection using Deep Convolutional Neural Networks (DD-CNN) project, which uses convolutional neural networks to analyze retinal images and classify them as diabetic or non-diabetic.

Conclusion:

The field of biomedical deep learning has experienced significant advancements in recent years, leading to groundbreaking discoveries and innovations. These advancements have been driven by the increasing availability of large datasets, advanced computing power, and innovative algorithms. The techniques, platforms, and tools used in biomedical deep learning systems have evolved to include deep learning, convolutional neural networks, recurrent neural networks, generative adversarial networks, TensorFlow, PyTorch, Keras, OpenCV, NumPy, and Matplotlib. The case studies presented in this article demonstrate the practical applications of these technologies in the fields of breast cancer detection, heart disease detection, and diabetes detection. As the field continues to evolve, we can expect to see even more exciting developments and breakthroughs in the years to come.

Weight: 689g
Dimension: 234 x 156 (mm)
ISBN-13: 9781771889988