Enhanced Disease Detection through Image Fusion in Solanum Tuberosum L.
An Improved Technique for Enhancement of Satellite Image
Magnetic Resonance and Computer Tomography Image Fusion using Novel Weight Maps Obtained by using Median and Guided Filters
Thresholding Techniques in Computer Vision Applications
Advancement in Brain Tumour Detection using Deep Learning Technique
Identification of Volcano Hotspots by using Resilient Back Propagation (RBP) Algorithm Via Satellite Images
Data Hiding in Encrypted Compressed Videos for Privacy Information Protection
Improved Video Watermarking using Discrete Cosine Transform
Contrast Enhancement based Brain Tumour MRI Image Segmentation and Detection with Low Power Consumption
Denoising of Images by Wavelets and Contourlets using Bi-Shrink Filter
Deep learning algorithms have simplified the process of creating indistinguishable synthetic videos, or deep fakes, because of the unparalleled increase in processing power. It is concerning because these face-swapped manipulations are often used in a variety of contexts, such as blackmail and political manipulation. This paper presents a revolutionary deep learning-based approach to accurately discriminating between real and Artificial Intelligence (AI)-generated false films. Using a ResNext Convolutional Neural Network (CNN) for frame-level feature extraction, this method makes use of an automated mechanism intended to identify replacement and re-enactment deep fakes. A Recurrent Neural Network (RNN) equipped with Long Short-Term Memory (LSTM) training is utilized to classify videos and distinguish between real and modified ones. The system demonstrates the effectiveness of a straightforward and reliable methodology, in addition to utilizing complex neural network topologies. Through testing, this paper showcases how well the system can accurately identify videos playing a crucial role in ongoing initiatives to combat the increasing dangers posed by the proliferation of deep fake content in society.
An attempt is made in this paper to diagnose brain-related diseases like sarcoma, fatal stroke disease, cerebral disease, and Alzheimer's disease by using saliency information from magnetic resonance and computed tomography source images. The saliency information for each source image is computed using guided and median filters. The obtained saliency maps are used for computing the weight maps of each source image by using image statistics. The obtained weights are used to fuse the approximate and detailed layers of the source images by using the weighted average fusion technique. The proposed algorithm is simulated in MATLAB for various benchmark data sets of brains taken from the brain atlas provided by Harvard Medical School, available at https://www.med.harvard.edu/aanlib. In order to test the efficacy of the novel method, comparative analysis is performed in terms of image quality assessment metrics like mean, mutual information, average gradient, standard deviation, spatial frequency, etc. From the analysis, this paper concludes that the proposed algorithm improved gradient information in the fused image by 35.7%, entropy by 5.7%, spatial frequency by 32.7%, edge strength by 14.5%, and minimized the information loss by 43.6%. Therefore, the novel method of weight map computation produces a detailed and noise-free image, which is helpful for better diagnosis in clinical applications.
Denoising is an essential step in data mining. It makes an effort to remove noise from the picture without sacrificing any of the important elements. The primary method utilized in this project to assess the effectiveness of phocomelia images is Kmeans clustering. The PSNR and MSE values of the denoised image are computed. In the end, the best technique for denoising medical images is chosen based on the PSNR values from the collection of patient data necessary for the fitting of upper limb devices. Using image processing, the bone health of phocomelia patients are evaluated and specific information extracted from MRI scans. Electromyography (EMG) sensors pick up electrical impulses, convert them into hand movements that the user wants, and flex the muscles in the residual limb directly below the elbow. The same muscles that enable hand function in humans are also felt by the system using microcontrollers. Through the integration of sophisticated sensor technologies with responsive prosthetic design idea, this newly developed technique enhances feedback from the proprioceptive system for patients with phocomelia, hence promoting natural movement and improving functional outcomes. As a result, it provides phocomelia patients a special method for acquiring superior result from biofeedback system.
Artificial Intelligence (AI) is a cutting-edge technology that analyzes complex data using computer algorithms. Diagnostic imaging is one of the most potential clinical uses of AI, and increasing effort is being put toward optimizing its functionality to make a wide range of clinical problems easier to identify and quantify. Research employing computeraided diagnostics has demonstrated exceptional precision, sensitivity, and specificity in identifying minute radiographic irregularities, which has promise for enhancing public health. However, lesion identification is often used to define result assessment in AI imaging research, neglecting the nature and biological aggressiveness of a lesion. This might lead to a distorted portrayal of AI's performance. Some AI imaging research evaluate clinically significant results, whereas others compute sensitivity and specificity to quantify diagnostic accuracy. Though AI frequently picks up on little changes to images, more significant outcome factors include newly discovered advanced disease, illnesses that need to be treated, or circumstances that might have an impact on long-term survival. AI-based research should concentrate on clinically significant events since they have a significant impact on quality of life, such as symptoms, the requirement for disease-modifying medication, and death. Numerous research have demonstrated that AI outperforms normal reading in terms of specificity and recall rates; nevertheless, the kind and biological aggressiveness of a lesion are often overlooked in the estimation of accuracy and sensitivity.
Medical imaging methods such as optical imaging have advanced significantly in their ability to diagnose and monitor various illnesses without any invasive procedures. Optical imaging offers a detailed view of anatomical structures and molecular processes, utilizing the unique properties of light and its interactions with biological tissues. This paper provides a comprehensive overview of optical imaging principles, including techniques like Optical Coherence Tomography (OCT), fluorescence imaging, and diffuse optical imaging. Optical imaging methods provide non-invasive, detailed views of biological tissues, enabling early disease detection and monitoring, thereby significantly advancing diagnostic capabilities and improving patient outcomes. These methods are used in the detection of diseases such as cancer, cardiovascular conditions, and neurological disorders.
