Advances in Deep Learning for Medical Image Analysis

The text discusses the use of classical probabilistic models, deep learning, and big data techniques to improve medical imaging and detect diseases. It covers a wide range of application areas, including signal analysis, cancer detection, diabetic cases, skin image analysis, Alzheimer's disease detection, coronary disease detection, medical image forensic, fetal anomaly detection, and plant phytology. It is a valuable resource for graduate students and academic researchers in the fields of electronics engineering, computer science, biomedical engineering, and electrical engineering.

Format: Hardback
Length: 154 pages
Publication date: 27 April 2022
Publisher: Taylor & Francis Ltd

The classical probabilistic model, deep learning, and big data techniques are explored in this comprehensive reference text as powerful tools for enhancing medical imaging and diagnosing a wide range of diseases. By leveraging the capabilities of deep learning, the text proposes solutions that require minimal human intervention and significantly reduce the time required for analysis. It covers a diverse array of application areas in medical imaging, including signal analysis, cancer detection, diabetic cases, skin image analysis, Alzheimer's disease detection, coronary disease detection, medical image forensic, fetal anomaly detection, and plant phytology. This text is particularly valuable for graduate students and academic researchers in the fields of electronics engineering, computer science, biomedical engineering, and electrical engineering.

The classical probabilistic model is a fundamental framework for understanding the distribution of random variables and making predictions based on observed data. It relies on probability theory, which provides a mathematical framework for quantifying the likelihood of different outcomes. The model assumes that the underlying variables are independent and identically distributed (i.i.d.), meaning that their values follow a specific probability distribution.

Deep learning is a subset of machine learning that involves training artificial neural networks to perform complex tasks. These networks are composed of multiple layers of interconnected neurons that learn to recognize patterns in data and make predictions based on those patterns. Deep learning has revolutionized the field of computer vision, speech recognition, and natural language processing, among other areas.

Big data refers to the vast amounts of data generated by various sources, such as sensors, social media, and medical records. The analysis of big data has become increasingly important in recent years as it provides insights into complex systems and enables the development of new algorithms and models. Big data techniques include data mining, machine learning, and statistical analysis, among others.

In the field of medical imaging, deep learning techniques have been used to improve the accuracy and efficiency of imaging analysis. For example, convolutional neural networks (CNNs) have been used to classify medical images, such as X-rays, CT scans, and MRI scans. These networks can learn to recognize patterns in the images and make predictions about the presence of diseases or abnormalities.

Deep learning has also been used for cancer detection. CNNs can analyze large amounts of medical data, including histology images, genetic information, and patient outcomes, to identify patterns that are associated with cancer. This has led to the development of new cancer detection algorithms that are more accurate and efficient than traditional methods.

Diabetic cases can also be analyzed using deep learning techniques. CNNs can analyze retinal images, which are a common form of imaging used to detect diabetic retinopathy, a condition that can lead to blindness. By learning to recognize patterns in these images, CNNs can identify early signs of diabetic retinopathy and help prevent vision loss.

Skin image analysis is another area where deep learning has been used to improve medical imaging. CNNs can analyze skin images to detect skin lesions, such as melanoma and basal cell carcinoma. This has led to the development of new skin cancer detection algorithms that are more accurate and efficient than traditional methods.

Alzheimer's disease detection is another area where deep learning has been used to improve medical imaging. CNNs can analyze brain images, such as MRI scans, to identify patterns that are associated with Alzheimer's disease. This has led to the development of new Alzheimer's disease detection algorithms that are more accurate and efficient than traditional methods.

Coronary disease detection is another area where deep learning has been used to improve medical imaging. CNNs can analyze heart images, such as echocardiograms, to identify patterns that are associated with coronary artery disease. This has led to the development of new coronary artery disease detection algorithms that are more accurate and efficient than traditional methods.

Medical image forensic is another area where deep learning has been used to improve medical imaging. CNNs can analyze medical images to identify artifacts, such as compression artifacts, that can interfere with the accuracy of imaging analysis. This has led to the development of new medical image forensic algorithms that are more accurate and efficient than traditional methods.

Fetal anomaly detection is another area where deep learning has been used to improve medical imaging. CNNs can analyze ultrasound images to identify fetal anomalies, such as congenital heart defects and neural tube defects. This has led to the development of new fetal anomaly detection algorithms that are more accurate and efficient than traditional methods.

Plant phytology is another area where deep learning has been used to improve medical imaging. CNNs can analyze plant images to identify plant species and identify patterns that are associated with plant diseases. This has led to the development of new plant phytology detection algorithms that are more accurate and efficient than traditional methods.

In conclusion, the classical probabilistic model, deep learning, and big data techniques are powerful tools for enhancing medical imaging and diagnosing a wide range of diseases. By leveraging the capabilities of deep learning, the text proposes solutions that require minimal human intervention and significantly reduce the time required for analysis. The text covers a diverse array of application areas in medical imaging, including signal analysis, cancer detection, diabetic cases, skin image analysis, Alzheimer's disease detection, coronary disease detection, medical image forensic, fetal anomaly detection, and plant phytology. This text is particularly valuable for graduate students and academic researchers in the fields of electronics engineering, computer science, biomedical engineering, and electrical engineering.

Weight: 392g
Dimension: 241 x 159 x 16 (mm)
ISBN-13: 9781032137162