Computer-aided gastrointestinal diseases analysis from wireless capsule endoscopy: a framework of best features selection
The continuous improvements in the area of medical imaging, makes the patient monitoring
a crucial concern. The internet of things (IoT) embedded in a medical technologies to collect …
a crucial concern. The internet of things (IoT) embedded in a medical technologies to collect …
Gastrointestinal tract disease classification from wireless endoscopy images using pretrained deep learning model
J Yogapriya, V Chandran, MG Sumithra… - … methods in medicine, 2021 - Wiley Online Library
Wireless capsule endoscopy is a noninvasive wireless imaging technology that becomes
increasingly popular in recent years. One of the major drawbacks of this technology is that it …
increasingly popular in recent years. One of the major drawbacks of this technology is that it …
[HTML][HTML] U-Net model with transfer learning model as a backbone for segmentation of gastrointestinal tract
The human gastrointestinal (GI) tract is an important part of the body. According to World
Health Organization (WHO) research, GI tract infections kill 1.8 million people each year. In …
Health Organization (WHO) research, GI tract infections kill 1.8 million people each year. In …
Wavelet transform and deep convolutional neural network-based smart healthcare system for gastrointestinal disease detection
This work presents a smart healthcare system for the detection of various abnormalities
present in the gastrointestinal (GI) region with the help of time–frequency analysis and …
present in the gastrointestinal (GI) region with the help of time–frequency analysis and …
[HTML][HTML] Automated identification of human gastrointestinal tract abnormalities based on deep convolutional neural network with endoscopic images
As a powerful analytic tool for medical image analysis, particularly for endoscopic image
interpretation, deep convolutional neural network (DCNN) has gained remarkable attention …
interpretation, deep convolutional neural network (DCNN) has gained remarkable attention …
An extensive study on cross-dataset bias and evaluation metrics interpretation for machine learning applied to gastrointestinal tract abnormality classification
Precise and efficient automated identification of gastrointestinal (GI) tract diseases can help
doctors treat more patients and improve the rate of disease detection and identification …
doctors treat more patients and improve the rate of disease detection and identification …
[HTML][HTML] GIT-Net: an ensemble deep learning-based GI tract classification of endoscopic images
H Gunasekaran, K Ramalakshmi, DK Swaminathan… - Bioengineering, 2023 - mdpi.com
This paper presents an ensemble of pre-trained models for the accurate classification of
endoscopic images associated with Gastrointestinal (GI) diseases and illnesses. In this …
endoscopic images associated with Gastrointestinal (GI) diseases and illnesses. In this …
Diagnosis of ulcerative colitis from endoscopic images based on deep learning
X Luo, J Zhang, Z Li, R Yang - Biomedical Signal Processing and Control, 2022 - Elsevier
Aims Evaluating the endoscopic images of patients with ulcerative colitis can effectively
determine a reasonable treatment plan. However, the endoscopic evaluation is usually …
determine a reasonable treatment plan. However, the endoscopic evaluation is usually …
[HTML][HTML] GASTRO-CADx: a three stages framework for diagnosing gastrointestinal diseases
O Attallah, M Sharkas - PeerJ Computer Science, 2021 - peerj.com
Gastrointestinal (GI) diseases are common illnesses that affect the GI tract. Diagnosing these
GI diseases is quite expensive, complicated, and challenging. A computer-aided diagnosis …
GI diseases is quite expensive, complicated, and challenging. A computer-aided diagnosis …
Artificial intelligence for colonoscopy: past, present, and future
During the past decades, many automated image analysis methods have been developed
for colonoscopy. Real-time implementation of the most promising methods during …
for colonoscopy. Real-time implementation of the most promising methods during …