Effectual seizure detection using MBBF-GPSO with CNN network.

EEG is the most common test for diagnosing a seizure, where it presents information about the electrical activity of the brain. Automatic Seizure detection is one of the challenging tasks due to limitations of conventional methods with regard to inefficient feature selection, increased computational complexity and time and less accuracy. The situation calls for a practical framework to achieve better performance for detecting the seizure effectively.

Optimal Deep CNN-Based Vectorial Variation Filter for Medical Image Denoising.

Medical imaging has acquired more attention due to the emerging design of wireless technologies, the internet, and data storage. The reflection of these technologies has gained attraction in medicine and medical sciences facilitating the diagnosis and treatment of different diseases in an effective manner. However, medical images are vulnerable to noise, which can make the image unclear and perplex the identification.

Lung nodule segmentation using Salp Shuffled Shepherd Optimization Algorithm-based Generative Adversarial Network.

Lung nodule segmentation is an exciting area of research for the effective detection of lung cancer. One of the significant challenges in detecting lung cancer is Accuracy, which is affected due to the visual deviations and heterogeneity in the lung nodules. Hence, to improve the segmentation process's Accuracy, a Salp Shuffled Shepherd Optimization Algorithm-based Generative Adversarial Network (SSSOA-based GAN) model is developed in this research for lung nodule segmentation.

Arrhythmia Classification with ECG signals based on the Optimization-Enabled Deep Convolutional Neural Network.

Arrhythmia classification is the need of the hour as the world is reporting a higher death troll as a cause of cardiac diseases. Most of the existing methods developed for arrhythmia classification face a hectic challenge of classification accuracy and they raised the challenge of automatic monitoring and classification methods. Accordingly, the paper proposes the automatic arrhythmia classification strategy using the optimization-based deep convolutional neural network (deep CNN).