Search

Biomedical imaging devices, in general, have been made and used a lot lately to examine the insides of the body during diagnostic and analytic procedures. Biomedical imaging gives accurate information about metabolites, which can be used to find and classify diseases because it is not invasive. For the study of cervical cancer (CC), the pap smear is a crucial type of biological imaging. CC is a crucial reason to enhance the rate of women's mortalities. Proper screening of pap smear images is critical for assisting in the early detection and analysis of CC. Computer‐aided systems for cancerous cell recognition need well established artificial intelligence (AI) methods. In this study, we introduce an automated Cervical Precancerous Lesion Classification using Quantum Invasive Weed Optimization with Deep Learning (CPLC‐QIWODL) on biomedical pap smear images. The presented CPLC‐QIWODL technique examines the pap smear images for cervical cancer classification. To do so, the presented CPLC‐QIWODL technique pre‐processes the biomedical images using a Gabor filtering (GF) approach. Moreover, the CPLC‐QIWODL technique uses a deep convolutional neural network‐based SqueezeNet system for feature extraction. Furthermore, the hyperparameter tuning of the SqueezeNet methodology takes place using the QIWO technique, showing the novelty of the work. Finally, to classify CC, the deep variational autoencoder (DVAE) model is applied. The experimental result analysis of the CPLC‐QIWODL technique is tested using a benchmark medical image database. Extensive comparative results demonstrated the enhanced outcomes of the CPLC‐QIWODL technique over other existing algorithms, with a maximum accuracy of 99.07%. [ABSTRACT FROM AUTHOR]

The brain‐computer interface (BCI) enables individuals with impairments to interact with the real world without relying on the neuromuscular pathway. BCI leverages artificial intelligence (AI) models for control. It can capture brain activity patterns associated with mental processes and convert them into commands for actuators. One promising application of BCI is in rehabilitation centres. When compared to traditional methods integrated with machine learning (ML) approaches that were initially explored in the past decade, there is a relatively limited number of studies utilizing deep learning (DL) approaches in BCI applications. A novel approach has been developed for the automatic recognition of motor imagery (MI) tasks. This article introduces the improved sparrow search algorithm with deep transfer learning‐based EEG motor imagery classification (ISSADTL‐EEGMIC) algorithm for BCIs. The presented ISSADTL‐EEGMIC algorithm primarily focuses on the automated classification of motor imagery tasks. To achieve this, the ISSADTL‐EEGMIC method first preprocesses EEG signals using the wavelet packet decomposition (WPD) technique. Additionally, it employs the neural architecture search network (NASNet) technique for feature extraction. Moreover, the ISSADTL‐EEGMIC model performs the classification process using the stacked sparse autoencoder (SSAE) model. Furthermore, hyperparameter optimization of the SSAE model is performed using the ISSA technique, which helps achieve maximum classification performance. The results from the simulation of the ISSADTL‐EEGMIC algorithm have been examined using two benchmark datasets. The experimental findings suggest that it outperforms recent approaches in terms of performance. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR) and age related macular degeneration (AMD) becomes widespread microvascular illness among diabetic patients. Traditional retinal fundus image classification requires visual inspection by the professionals, which is time consuming and requires expert's knowledge. Earlier identification of retinal diseases is essential to delay or avoid vision deterioration and vision loss. The recently developed artificial intelligence (AI) and deep learning (DL) models can be employed for accurate retinal image classification. With this motivation, this study designs a new artificial intelligence with optimal deep convolutional neural network (AI‐ODCNN) technique for retinal fundus image classification. Primarily, the proposed model uses the Gaussian Blur based noise removal and contrast enhancement technique (CLAHE) based contrast enhancement technique to pre‐process the retinal fundus image. In addition, morphology and contour based image segmentation is performed. Moreover, the deep CNN with RMSProp Optimizer is employed for retinal fundus image classification. A wide range of simulations was performed on the automated retinal image analysis and structured analysis of the retina and the outcomes are examined with respect to various measures. The simulation outcomes ensured the better performance of the proposed approach related to other recent algorithms with maximum accuracy of 96.47%. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) related to the Internet of Things (IoT) have shown incredible growth with technological advancements, offering a greater number of applications all over the world. Sensor nodes are deployed closer to the base station (BS) in some scenarios and held accountable for transmitting data from neighboring and own nodes against the BS and depletes energy. In the network, this problem is referred to as the "hotspot issue" and it may be fixed using procedures that use unequal clustering. In this study, a novel approach for IoT-assisted wireless sensor networks called the Fire Hawk Optimization-based Unequal Clustering Scheme for Hotspot Mitigation (FHOUCS-HSM) is developed. In the presented FHOUCS-HSM model, a first-order radio energy model has been utilized. The FHOUCS-HSM technique follows the foraging characteristics of whistling kites, black kites, and brown falcons. The design of the FHO algorithm for unequal clustering shows the novelty of the work. In addition to this, the FHOUCS-HSM model computes a fitness function for the use of uneven cluster size and the selection of cluster heads (CH). The comparative research highlights the advantages that the FHOUCS-HSM technique has over many models that are already in use. [ABSTRACT FROM AUTHOR]

Retinal fundus images are widely employed to screen for various eye diseases, giving them significant clinical importance. Investigation of medical images has been considerably enhanced by using deep learning (DL) approaches which enables automated learning of the related features for particular tasks rather than handcrafted techniques. In this view, this study develops an Optimal Deep Convolutional Neural Network for Retinal Fundus Image Classification (ODCNN-RFIC) model. The presented technique involves pre-processing in two stages: Guided Filter (GF) and Adaptive Median Filter (AMF). The U-Net technique is employed for image segmentation, allowing the infected regions to be detected appropriately. Additionally, the EfficientNet feature extractor is utilized for generating feature vectors. Finally, the mayfly optimization with kernel extreme learning machine (MFO-KELM) model is applied as a classification model. The experimental values highlighted the superior performance of the proposed method on existing algorithms. [ABSTRACT FROM AUTHOR]