Performance analysis of image retrieval system using deep learning techniques.

The image retrieval is the process of retrieving the relevant images to the query image with minimal searching time in internet. The problem of the conventional Content-Based Image Retrieval (CBIR) system is that they produce retrieval results for either colour images or grey scale images alone. Moreover, the CBIR system is more complex which consumes more time period for producing the significant retrieval results.

Integration of silver nanostructures in wireless sensor networks for enhanced biochemical sensing.

Integrating noble metal nanostructures, specifically silver nanoparticles, into sensor designs has proven to enhance sensor performance across key metrics, including response time, stability, and sensitivity. However, a critical gap remains in understanding the unique contributions of various synthesis parameters on these enhancements. This study addresses this gap by examining how factors such as temperature, growth time, and choice of capping agents influence nanostructure shape and size, optimizing sensor performance for diverse conditions.

Detection and Segmentation of Glioma Tumors Utilizing a UNet Convolutional Neural Network Approach with Non-Subsampled Shearlet Transform.

The prompt and precise identification and delineation of tumor regions within glioma brain images are critical for mitigating the risks associated with this life-threatening ailment. In this study, we employ the UNet convolutional neural network (CNN) architecture for glioma tumor detection. Our proposed methodology comprises a transformation module, a feature extraction module, and a tumor segmentation module.

An automated cervical cancer diagnosis using genetic algorithm and CANFIS approaches.

Background: Cervical malignancy is considered among the most perilous cancers affecting women in numerous East African and South Asian nations, both in terms of its prevalence and fatality rates.

Objective: This research aims to propose an efficient automated system for the segmentation of cancerous regions in cervical images.

Methods: The proposed techniques encompass preprocessing, feature extraction with an optimized feature set, classification, and segmentation.

Meningioma brain tumor detection and classification using hybrid CNN method and RIDGELET transform.

The detection of meningioma tumors is the most crucial task compared with other tumors because of their lower pixel intensity. Modern medical platforms require a fully automated system for meningioma detection. Hence, this study proposes a novel and highly efficient hybrid Convolutional neural network (HCNN) classifier to distinguish meningioma brain images from non-meningioma brain images.