Deep learning models for digital image processing: a review
R Archana, PSE Jeevaraj - Artificial Intelligence Review, 2024 - Springer
Within the domain of image processing, a wide array of methodologies is dedicated to tasks
including denoising, enhancement, segmentation, feature extraction, and classification …
including denoising, enhancement, segmentation, feature extraction, and classification …
Classifying cardiac arrhythmia from ECG signal using 1D CNN deep learning model
Blood circulation depends critically on electrical activation, where any disturbance in the
orderly pattern of the heart's propagating wave of excitation can lead to arrhythmias …
orderly pattern of the heart's propagating wave of excitation can lead to arrhythmias …
A survey and taxonomy of 2.5 D approaches for lung segmentation and nodule detection in CT images
RJ Suji, SS Bhadauria, WW Godfrey - Computers in Biology and Medicine, 2023 - Elsevier
CAD systems for lung cancer diagnosis and detection can significantly offer unbiased,
infatiguable diagnostics with minimal variance, decreasing the mortality rate and the five …
infatiguable diagnostics with minimal variance, decreasing the mortality rate and the five …
Multi-techniques for analyzing x-ray images for early detection and differentiation of pneumonia and tuberculosis based on hybrid features
An infectious disease called tuberculosis (TB) exhibits pneumonia-like symptoms and traits.
One of the most important methods for identifying and diagnosing pneumonia and …
One of the most important methods for identifying and diagnosing pneumonia and …
[HTML][HTML] Machine learning based medical image deepfake detection: A comparative study
S Solaiyappan, Y Wen - Machine Learning with Applications, 2022 - Elsevier
Deep generative networks in recent years have reinforced the need for caution while
consuming various modalities of digital information. One avenue of deepfake creation is …
consuming various modalities of digital information. One avenue of deepfake creation is …
[HTML][HTML] An improved SqueezeNet model for the diagnosis of lung cancer in CT scans
M Tsivgoulis, T Papastergiou… - Machine Learning with …, 2022 - Elsevier
Lung cancer is the leading cause of cancer deaths nowadays and its early detection and
treatment plays an important role in survival of patients. The main challenge is to acquire an …
treatment plays an important role in survival of patients. The main challenge is to acquire an …
Deep neural network for lung image segmentation on chest x-ray
COVID-19 patients require effective diagnostic methods, which are currently in short supply.
In this study, we explained how to accurately identify the lung regions on the X-ray scans of …
In this study, we explained how to accurately identify the lung regions on the X-ray scans of …
Skin disease classification using two path deep transfer learning models
R Chandna, A Bansal, A Kumar, S Hardia… - Journal of Knowledge …, 2024 - jklst.org
Skin diseases are among the most common diseases that affect millions of lives per year, yet
diagnosing these has several complexities even for trained dermatologists due to …
diagnosing these has several complexities even for trained dermatologists due to …
A hybrid CNN-Random Forest algorithm for bacterial spore segmentation and classification in TEM images
We present a new approach to segment and classify bacterial spore layers from
Transmission Electron Microscopy (TEM) images using a hybrid Convolutional Neural …
Transmission Electron Microscopy (TEM) images using a hybrid Convolutional Neural …
Rethinking densely connected convolutional networks for diagnosing infectious diseases
Due to its high transmissibility, the COVID-19 pandemic has placed an unprecedented
burden on healthcare systems worldwide. X-ray imaging of the chest has emerged as a …
burden on healthcare systems worldwide. X-ray imaging of the chest has emerged as a …