A novel approach to brain tumor detection using K-Means++, SGLDM, ResNet50, and synthetic data augmentation.

Brain tumors are abnormal cell growths in the brain, posing significant treatment challenges. Accurate early detection using non-invasive methods is crucial for effective treatment. This research focuses on improving the early detection of brain tumors in MRI images through advanced deep-learning techniques.

Effective image fusion strategies in scientific signal processing disciplines: Application to cancer and carcinoma treatment planning.

Multimodal medical image fusion is a perennially prominent research topic that can obtain informative medical images and aid radiologists in diagnosing and treating disease more effectively. However, the recent state-of-the-art methods extract and fuse features by subjectively defining constraints, which easily distort the exclusive information of source images. To overcome these problems and get a better fusion method, this study proposes a 2D data fusion method that uses salient structure extraction (SSE) and a swift algorithm via normalized convolution to fuse different types of medical images.

ProTect: a hybrid deep learning model for proactive detection of cyberbullying on social media.

The emergence of social media has given rise to a variety of networking and communication opportunities, as well as the well-known issue of cyberbullying, which is continuously on the rise in the current world. Researchers have been actively addressing cyberbullying for a long time by applying machine learning and deep learning techniques. However, although these algorithms have performed well on artificial datasets, they do not provide similar results when applied to real-time datasets with high levels of noise and imbalance.

Optimizing QoS and security in agriculture IoT deployments: A bioinspired Q-learning model with customized shards.

Agriculture Internet of Things (AIoTs) deployments require design of high-efficiency Quality of Service (QoS) & security models that can provide stable network performance even under large-scale communication requests. Existing security models that use blockchains are either highly complex or require large delays & have higher energy consumption for larger networks. Moreover, the efficiency of these models depends directly on consensus-efficiency & miner-efficiency, which restricts their scalability under real-time scenarios.

Personalization-based deep hybrid E-learning model for online course recommendation system.

Deep learning, a subset of artificial intelligence, gives easy way for the analytical and physical tasks to be done automatically. There is a less necessity for human intervention while performing these tasks. Deep hybrid learning is a blended approach to combine machine learning with deep learning.

Unfolded deep kernel estimation-attention UNet-based retinal image segmentation.

Retinal vessel segmentation is a critical process in the automated inquiry of fundus images to screen and diagnose diabetic retinopathy. It is a widespread complication of diabetes that causes sudden vision loss. Automated retinal vessel segmentation can help to detect these changes more accurately and quickly than manual evaluation by an ophthalmologist.

Towards reliable IoT communication and robust security: investigating trusted schemes in the internet of medical things using blockchain.

The Internet of Things (IoT) is evolving in various sectors such as industries, healthcare, smart homes, and societies. Billions and trillions of IoT devices are used in e-health systems, known as the Internet of Medical Things (IoMT), to improve communication processes in the network. Scientists and researchers have proposed various methods and schemes to ensure automatic monitoring, communication, diagnosis, and even operating on patients at a distance.

A constructive non-local means algorithm for low-dose computed tomography denoising with morphological residual processing.

Low-dose computed tomography (LDCT) has attracted significant attention in the domain of medical imaging due to the inherent risks of normal-dose computed tomography (NDCT) based X-ray radiations to patients. However, reducing radiation dose in CT imaging produces noise and artifacts that degrade image quality and subsequently hinders medical disease diagnostic performance. In order to address these problems, this research article presents a competent low-dose computed tomography image denoising algorithm based on a constructive non-local means algorithm with morphological residual processing to achieve the task of removing noise from the LDCT images.

Efficient Deep Learning Based Hybrid Model to Detect Obstructive Sleep Apnea.

An increasing number of patients and a lack of awareness about obstructive sleep apnea is a point of concern for the healthcare industry. Polysomnography is recommended by health experts to detect obstructive sleep apnea. The patient is paired up with devices that track patterns and activities during their sleep.

Design of a medical decision-supporting system for the identification of brain tumors using entropy-based thresholding and non-local texture features.

Introduction: Brain tumors arise due to abnormal growth of cells at any brain location with uneven boundaries and shapes. Usually, they proliferate rapidly, and their size increases by approximately 1.4% a day, resulting in invisible illness and psychological and behavioral changes in the human body.

Multimodal Medical Image Fusion Based on Content-based and PCA-sigmoid.

Objective: The objective of any multimodal medical image fusion algorithm is to assist a radiologist for better decision-making during the diagnosis and therapy by integrating the anatomical (magnetic resonance imaging) and functional (positron emission tomography/ single-photon emission computed tomography) information.

Methods: We proposed a new medical image fusion method based on content-based decomposition, Principal Component Analysis (PCA), and sigmoid function. We considered Empirical Wavelet Transform (EWT) for content-based decomposition purposes since it can preserve crucial medical image information such as edges and corners.