Medical image segmentation using deep semantic-based methods: A review of techniques, applications and emerging trends
Semantic-based segmentation (Semseg) methods play an essential part in medical imaging
analysis to improve the diagnostic process. In Semseg technique, every pixel of an image is …
analysis to improve the diagnostic process. In Semseg technique, every pixel of an image is …
[Retracted] Machine Learning‐Based Automated Diagnostic Systems Developed for Heart Failure Prediction Using Different Types of Data Modalities: A Systematic …
A Javeed, SU Khan, L Ali, S Ali… - … Methods in Medicine, 2022 - Wiley Online Library
One of the leading causes of deaths around the globe is heart disease. Heart is an organ
that is responsible for the supply of blood to each part of the body. Coronary artery disease …
that is responsible for the supply of blood to each part of the body. Coronary artery disease …
Pneumonia detection in chest X-ray images using an ensemble of deep learning models
Pneumonia is a respiratory infection caused by bacteria or viruses; it affects many
individuals, especially in developing and underdeveloped nations, where high levels of …
individuals, especially in developing and underdeveloped nations, where high levels of …
A comparative performance analysis of data resampling methods on imbalance medical data
Medical datasets are usually imbalanced, where negative cases severely outnumber
positive cases. Therefore, it is essential to deal with this data skew problem when training …
positive cases. Therefore, it is essential to deal with this data skew problem when training …
Brain tumor and glioma grade classification using Gaussian convolutional neural network
Understanding brain diseases such as categorizing Brain-Tumor (BT) is critical to assess the
tumors and facilitate the patient with proper cure as per their categorizations. Numerous …
tumors and facilitate the patient with proper cure as per their categorizations. Numerous …
VGG19 network assisted joint segmentation and classification of lung nodules in CT images
Pulmonary nodule is one of the lung diseases and its early diagnosis and treatment are
essential to cure the patient. This paper introduces a deep learning framework to support the …
essential to cure the patient. This paper introduces a deep learning framework to support the …
A deep learning approach for liver and tumor segmentation in CT images using ResUNet
According to the most recent estimates from global cancer statistics for 2020, liver cancer is
the ninth most common cancer in women. Segmenting the liver is difficult, and segmenting …
the ninth most common cancer in women. Segmenting the liver is difficult, and segmenting …
A hybrid deep learning model for effective segmentation and classification of lung nodules from CT images
M Murugesan, K Kaliannan, S Balraj… - Journal of Intelligent …, 2022 - content.iospress.com
Deep learning algorithms will be used to detect lung nodule anomalies at an earlier stage.
The primary goal of this effort is to properly identify lung cancer, which is critical in …
The primary goal of this effort is to properly identify lung cancer, which is critical in …
Time series forecasting of COVID-19 transmission in Asia Pacific countries using deep neural networks
The novel human coronavirus disease COVID-19 has become the fifth documented
pandemic since the 1918 flu pandemic. COVID-19 was first reported in Wuhan, China, and …
pandemic since the 1918 flu pandemic. COVID-19 was first reported in Wuhan, China, and …
Dilated semantic segmentation for breast ultrasonic lesion detection using parallel feature fusion
Breast cancer is becoming more dangerous by the day. The death rate in developing
countries is rapidly increasing. As a result, early detection of breast cancer is critical, leading …
countries is rapidly increasing. As a result, early detection of breast cancer is critical, leading …