Hybrid Approach for Denoising and Segmentation: N2S with Swin Transformer-Enhanced U-Net
ANIMAL DETECTION IN FEILDS USING IMAGE PROCESSING
Malaria Detection Using Advanced U-Net Deep Learning Model
MULTILEVEL THRESHOLDING USING K-POINT STRATEGY IMPROVED CONVERGENCE BASED WHALE OPTIMIZATION ALGORITHM FOR IMAGE SEGMENTATION
Infrared and Visible Image Fusion Using Contrast and Edge-Preserving Filters with Image Statistics
Attention-Enhanced Deep Learning Model for Parkinson’s Diagnosis
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
Accurate segmentation in medical imaging, particularly for modalities such as Chest X-rays, CT scans, and microscopic images, is critical for diagnosis and treatment. However, noisy and low-quality data can significantly affect performance. This paper presents a novel framework that integrates Noise2Split denoising with a Hybrid Swin Transformer U-Net to enhance segmentation accuracy in these challenging medical imaging tasks. By combining Noise2Split’s effective noise reduction with the Swin Transformer’s advanced feature extraction and U-Net’s robust segmentation architecture, the model efficiently addresses both noise and segmentation challenges. The Swin Transformer effectively captures both local and global context, while the skip connections in U-Net contribute to recovering detailed high-resolution features.Extensive experiments on Chest X-rays, CT scans, and microscopic images demonstrate that this integrated model performs better than traditional methods with regards to segmentation accuracy, making it a valuable tool for clinical applications where imaging quality is compromised.
One of the primary requirements for sustaining a livelihood isagriculture. Low productivity of crops is one of the issues faced by the growers in our country. Crops destroyed by wild creatures is a major issue in low productivity. The agrarian fields must be defended from any undesirable interruption from creatures. In traditional styles, growers use crackers, electrical walls, direct observation etc., to keep creatures down from their fields but it's a threat factor that harms both humans and creatures. Our proposed system detects the presence of creatures using Image Processing and Machine Learning. Every time, crop damaged by wild creatures is dramatically adding in India. It frequently poses pitfalls to humans and creatures. Since further and further wild creatures are causing damage to their civilization; humans couldn't tolerate it. thus, they bear an effective medium to overcome this situation. With that background, the ideal of this study is to descry wild creatures before entering into the crop fields and enforcing applicable dread- down mechanisms in real- time. This paper presents an overview of the methodologies employed in this prototype model , including image segmentation, point birth, and bracket ways. Overall, this study highlights the significance of image processing technologies in advancing our understanding of this model and promoting sustainable relations between humans and wildlife.
Malaria continues to be a major global health issue that needs prompt and precise diagnosis. An enhanced U-Net deep learning model for the identification of malaria from microscopic blood smear pictures is proposed in this paper. U-Net performs better in segmentation-based feature extraction than standard deep learning methods, increasing the accuracy of detection. U-Net effectively localizes diseased regions, improving precision, whereas CNN concentrates on classification and ANN struggles with complex spatial patterns. According to experimental results, U-Net performs better in terms of sensitivity and specificity than ANN and CNN. The model guarantees accurate detection, minimizes human error, and cuts down on diagnostic time. It is appropriate for real-world deployment due to its computational efficiency, particularly in environments with restricted resources. Techniques for data augmentation enhance generalization even more, strengthening its resilience over a range of datasets. This revolutionary method for automated malaria screening is based on deep learning.
This study introduces a novel multilevel image segmentation approach based on an enhanced Whale Optimization Algorithm (WOA). While WOA has shown promise in various optimization tasks, its performance can be limited by a tendency to get trapped in local optima. To address this challenge, we propose the K-point Strategy Improved Convergence WOA (KSICWOA), which enhances optimization efficiency by incorporating a nonlinear convergence factor, an adaptive weight coefficient, and a k-point initialization strategy. The proposed KSICWOA is then applied alongside Otsu’s cross variance and Kapur’s entropy as objective functions to determine optimal thresholds for multilevel grayscale image segmentation. Experimental results on benchmark functions as well as real time images demonstrate that KSICWOA surpasses conventional optimization techniques in terms of search accuracy and convergence speed while effectively avoiding local optima. Additionally, tests conducted on standard image segmentation datasets confirm that the KSICWOA-Kapur method accurately and efficiently identifies multilevel thresholds.
Infrared (IR) and visible image fusion is a crucial technique in data fusion and image processing. It allows for the accurate integration of thermal radiation and texture details from source images. However, current methods often overlook the challenge of high-contrast fusion, resulting in suboptimal performance when replacing thermal radiation target information in IR images with high-contrast information from visible images. To overcome this limitation, we have developed a contrast-balanced framework for IR and visible image fusion. Our innovative approach includes a contrast balance strategy for processing visible images, reducing energy while compensating for overexposed areas in detail. Additionally, a contrast-preserving guided filter decomposes the image into energy-detail layers to filter high contrast and information effectively. To extract active information from the detail layer and brightness information from the energy layer, we introduce Image Statistics technique and a Gaussian distribution of image entropy scheme for fusing the detail and energy layers. The final fused result is achieved by combining the detail and energy layers.Comprehensive experimental results show that our method significantly diminishes contrast issues while maintaining details. Additionally, our approach outperformed leading techniques in both qualitative and quantitative evaluations.
This study presents an AI-based system for the early detection of Parkinson’s disease using deep learning models, InceptionV3 and Xception, with an Attention Mechanism. The system analyzes hand-drawn spiral images, which act as biomarkers for Parkinson’s symptoms like tremors and micrographia. The proposed model extracts critical features from these images using pre-trained convolutional neural networks (CNNs) enhanced with attention layers, ensuring effective classification. The dataset consists of spiral drawings from both healthy individuals and Parkinson’s patients, enabling the model to learn distinguishing features. The InceptionV3 model achieved 100% accuracy, while Xception attained 88% accuracy in Parkinson’s detection. To evaluate performance, accuracy vs. epoch and loss vs. epoch graphs were plotted to track learning trends, a confusion matrix was generated to analyze misclassifications, and a classification report provided insights into precision, recall, and F1-score. A comparative bar chart further highlighted the performance difference between the InceptionV3 and Xception models. This AI-driven approach offers a non-invasive, cost-effective, and automated diagnostic tool, improving early diagnosis and assisting healthcare professionals in timely intervention.
