3 research outputs found
Ultrafast and Ultralight Network-Based Intelligent System for Real-time Diagnosis of Ear diseases in Any Devices
Traditional ear disease diagnosis heavily depends on experienced specialists
and specialized equipment, frequently resulting in misdiagnoses, treatment
delays, and financial burdens for some patients. Utilizing deep learning models
for efficient ear disease diagnosis has proven effective and affordable.
However, existing research overlooked model inference speed and parameter size
required for deployment. To tackle these challenges, we constructed a
large-scale dataset comprising eight ear disease categories and normal ear
canal samples from two hospitals. Inspired by ShuffleNetV2, we developed
Best-EarNet, an ultrafast and ultralight network enabling real-time ear disease
diagnosis. Best-EarNet incorporates the novel Local-Global Spatial Feature
Fusion Module which can capture global and local spatial information
simultaneously and guide the network to focus on crucial regions within feature
maps at various levels, mitigating low accuracy issues. Moreover, our network
uses multiple auxiliary classification heads for efficient parameter
optimization. With 0.77M parameters, Best-EarNet achieves an average frames per
second of 80 on CPU. Employing transfer learning and five-fold cross-validation
with 22,581 images from Hospital-1, the model achieves an impressive 95.23%
accuracy. External testing on 1,652 images from Hospital-2 validates its
performance, yielding 92.14% accuracy. Compared to state-of-the-art networks,
Best-EarNet establishes a new state-of-the-art (SOTA) in practical
applications. Most importantly, we developed an intelligent diagnosis system
called Ear Keeper, which can be deployed on common electronic devices. By
manipulating a compact electronic otoscope, users can perform comprehensive
scanning and diagnosis of the ear canal using real-time video. This study
provides a novel paradigm for ear endoscopy and other medical endoscopic image
recognition applications.Comment: This manuscript has been submitted to Neural Network
Exploring the applicability of machine learning based artificial intelligence in the analysis of cardiovascular imaging
Worldwide, the prevalence of cardiovascular diseases has doubled, demanding new diagnostic tools. Artificial intelligence, especially machine learning and deep learning, offers innovative possibilities for medical research. Despite historical challenges, such as a lack of data, these techniques have potential for cardiovascular research. This thesis explores the application of machine learning and deep learning in cardiology, focusing on automation and decision support in cardiovascular imaging.Part I of this thesis focuses on automating cardiovascular MRI analysis. A deep learning model was developed to analyze the ascending aorta in cardiovascular MRI images. The model's results were used to investigate connections between genetic material and aortic properties, and between aortic properties and cardiovascular diseases and mortality. A second model was developed to select MRI images suitable for analyzing the pulmonary artery.Part II focuses on decision support in nuclear cardiovascular imaging. A first machine learning model was developed to predict myocardial ischemia based on CTA variables. In addition, a deep neural network was used to identify reduced oxygen supply through the arteries supplying oxygen-rich blood to the heart and cardiovascular risk features using PET images.This thesis successfully explores the possibilities of machine learning and deep learning in cardiovascular research, with a focus on automated analysis and decision support
Exploring the applicability of machine learning based artificial intelligence in the analysis of cardiovascular imaging
Worldwide, the prevalence of cardiovascular diseases has doubled, demanding new diagnostic tools. Artificial intelligence, especially machine learning and deep learning, offers innovative possibilities for medical research. Despite historical challenges, such as a lack of data, these techniques have potential for cardiovascular research. This thesis explores the application of machine learning and deep learning in cardiology, focusing on automation and decision support in cardiovascular imaging.Part I of this thesis focuses on automating cardiovascular MRI analysis. A deep learning model was developed to analyze the ascending aorta in cardiovascular MRI images. The model's results were used to investigate connections between genetic material and aortic properties, and between aortic properties and cardiovascular diseases and mortality. A second model was developed to select MRI images suitable for analyzing the pulmonary artery.Part II focuses on decision support in nuclear cardiovascular imaging. A first machine learning model was developed to predict myocardial ischemia based on CTA variables. In addition, a deep neural network was used to identify reduced oxygen supply through the arteries supplying oxygen-rich blood to the heart and cardiovascular risk features using PET images.This thesis successfully explores the possibilities of machine learning and deep learning in cardiovascular research, with a focus on automated analysis and decision support