Sleep apnea is a disorder that has the potential to be life-threatening, that is characterized by irregular breathing patterns. In order to improve the diagnosis and prediction of sleep apnea, a study was conducted to develop a high-accuracy detection method using machine learning. This method involved the use of a convolutional neural network classifier, which was trained using public data sets of ECG signals from both apnea patients and healthy volunteers. The CNN model was able to attain a level of accuracy of 94.12% using the Xception model and 91.18% using the ResNet50 model. According to the study’s findings, using deep learning techniques can be a helpful strategy to enhance sleep apnea diagnosis and prediction.
Epilepsy is a neurological condition caused by sudden onsets of electrical activity in the brain. This results in frequent, uncommon seizures, which can lead to severe physical consequences. In a clinical setting, data recorded using EEG (Electroencephalogram) is used to help diagnose the condition. This research focuses on the use of Short-Term Fourier transform (STFT) and feature extraction in the EEG data for the use in a majority voting model using logistic regression (LR) to detect the presence of epileptic seizures in the five EEG frequency bands ( i.e. Alpha, Beta, Gamma, Delta, and Theta). To quantify, a number of evaluation metrics have been calculated. Overall, the model was able to achieve an accuracy of up to 92%.
Artificial intelligence (AI) i n h ealthcare i s a constantly evolving field that must be explored. Be cause of its practicality and usefulness in estimating various ailments, focused research on AI, specifically deep l earning, is dominating. High blood pressure (BP), also known as hypertension, is a serious health condition. It causes serious issues such as heart attacks, strokes, and even death. As a result, blood pressure should be constantly monitored. The proposed study uses famous CNN models for blood pressure detection and states the results of two main CNN models. Inception-V4 and Xception achieved an accuracy of 96% and 98.8%, respectively. Other performance metrics have been calculated and discussed.This study demonstrates the effectiveness of using deep learning techniques to aid in the diagnosis and prediction of hypertension.
KEYWORDS: Performance modeling, Retina, Data modeling, Convolutional neural networks, Machine learning, Deep learning, Visual process modeling, Image classification, Binary data
Due to high blood sugar levels, diabetic retinopathy (DR), a complication of diabetes, affects the retina in the back of the eye. It may cause blindness if undiagnosed and mistreated. The early detection and treatment of DR are made easier by retinal screening. This paper proposes using an image-based dataset to build different convolution neural network (CNN) models to detect DR in its early stages to ease the screening procedure. The accuracy achieved was 0.9615 using the VGG model and 0.9712 using the Inception-ResNet model. This study demonstrates the effectiveness of using deep learning techniques to aid in diagnosing and predicting diabetic retinopathy.
The most notable advancement in the 21st Century has been in artificial intelligence (AI). Despite how far AI has progressed, how it applies to healthcare remains a significant challenge for brilliant minds all over the world. A neurological condition known as epilepsy can strike a person at any time in their life. An individual with epilepsy therefore experiences frequent to infrequent seizures, which can occasionally result in death. Electroencephalogram (EEG) signals aid in the diagnosis of this condition. However, lengthy EEG signals frequently take a day or longer to detect this disorder, even for trained neurologists, and may even cause human error. Therefore, it is essential to create a reliable and computationally efficient system. This study aims to classify seizures by creating Convolutional Neural Network (CNN) Inception ResNet V2 and short-time Fourier transform (STFT) to extract the time-frequency plane from time domain signals. This study helped to better classify health and seizures by achieving up to 100% the highest classification accuracy.
Plastic pollution has emerged as one of the biggest environmentally threatening issues. Using image classification, the proposed study aids in categorizing the level of marine pollution in ocean underwater regions. This study classified the amount of pollution in the ocean using the two variants of Inception Convolutional Neural Network (CNN) models i.e., Inception- ResNet V2, and InceptionV3. High accuracies of up to 96.4% have been reported. This study will help researchers working in the field of water quality detection.
Marine pollution is a major environmental hazard and a serious healthcare, economic, and social issue. Machine learning (ML) and deep learning (DL) techniques can be used to automate marine waste removal and make the cleanup process more efficient. The proposed study uses image classification to help categorize the level of marine pollution in ocean underwater regions. The performance of two deep convolutional neural networks (VGG19 and ResNet50) is investigated in this study and VGG19 reported an accuracy of 98.1%.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.