AI Driven Detection and Remediation of Diabetic Foot Ulcer (DFU)
Advancements in Image Processing: Towards Near-Reversible Data Hiding and Enhanced Dehazing using Deep Learning
AI Based Sign Language Recognition System
Brain Tumour Detection using Deep Learning Technique
State-of-the-Art Deep Learning Techniques for Object Identification in Practical Applications
Landslide Susceptibility Mapping through Weightages Derived from Statistical Information Value Model
An Efficient Foot Ulcer Determination System for Diabetic Patients
Statistical Wavelet based Adaptive Noise Filtering Technique for MRI Modality
Real Time Sign Language: A Review
Remote Sensing Schemes Mingled with Information and Communication Technologies (ICTS) for Flood Disaster Management
FPGA Implementation of Shearlet Transform Based Invisible Image Watermarking Algorithm
A Comprehensive Study on Different Pattern Recognition Techniques
User Authentication and Identification Using NeuralNetwork
Flexible Generalized Mixture Model Cluster Analysis withElliptically-Contoured Distributions
Efficient Detection of Suspected areas in Mammographic Breast Cancer Images
Diabetic foot ulcers (DFUs) constitute a significant health concern in India, affecting a substantial portion of diabetic patients. Without prompt intervention, these ulcers can result in severe complications, including infection, gangrene, amputation, and chronic wounds. Approximately 72% of DFU patients test positive for multidrug-resistant organisms (MDROs), further elevating the risk of complications. Early detection is critical to preventing such outcomes. This prototype leverages artificial intelligence (AI) and deep learning techniques, specifically Convolutional Neural Networks (CNNs), for the detection and assessment of DFUs. By analyzing annotated medical images, the system accurately measures the size and depth of ulcers using CNNs. AI enables early diagnosis, facilitating timely and customized treatments, enhancing clinical decision-making, and mitigating the risks associated with advanced DFUs. The system employs an ESP32 camera to capture real-time images of the ulcers. Following image capture, the CNN algorithm performs image masking to isolate the ulcer region. The wound's contours are displayed on a terminal, and the severity percentage of the ulcer is calculated, along with recommended interventions based on the wound's stage. This approach not only reduces healthcare costs but also improves patient outcomes by preventing severe complications. The study underscores the importance of early diagnosis and highlights AI's potential in the effective management of DFUs.
In the age of digital transformation, image processing techniques play a crucial role in various applications, ranging from security to entertainment. This paper explores two significant advancements in the field: near-reversible data hiding schemes and deep learning-based single-image dehazing techniques. Near-reversible data hiding focuses on embedding secret information into digital images with minimal distortion, ensuring that the original image can be almost perfectly recovered. Conversely, deep learning-based single-image dehazing aims to enhance the quality and robustness of images affected by atmospheric haze, using reference images and advanced neural network architectures. This paper delves into the principles, methodologies, and applications of these cutting-edge techniques, shedding light on their potential impact on the future of image processing.
This study presents an AI-based system designed to facilitate communication between hearing-impaired and hearing individuals by translating sign language gestures into spoken English. The proposed system recognizes and decodes sign language motions captured through video inputs by utilizing deep learning techniques, specifically Convolutional Neural Networks (CNNs). The input video is first processed by the system using a variety of CNN models that have been trained to extract features. After identification, the signs are translated into appropriate text, which is then converted into speech by a Text-to-Speech (TTS) engine. The model can recognize various hand shapes, movements, and facial expressions, which are essential for accurate sign language interpretation, after being trained on a large dataset of annotated sign language gestures. Due to its real-time operation, the technology provides an effective communication method for individuals with hearing impairments. This approach offers a feasible solution for improving accessibility in social interactions, healthcare, education, and customer service by significantly reducing the communication barrier between hearing-impaired and hearing individuals.
Recent advancements in medical imaging technology, such as the integration of InceptionV3 algorithms with MRI scans, have revolutionized brain tumor detection. These algorithms leverage deep learning to analyze MRI images rapidly and accurately, aiding in the precise identification of potential tumors. This integration enhances the efficiency of radiologists, enabling timely interventions and improving patient outcomes. The seamless synergy between MRI technology and deep learning algorithms marks a significant leap forward in neurology, promising more personalized and effective care for patients with brain tumors. Ongoing innovation in medical imaging and AI holds great potential for further improving diagnostic accuracy and treatment effectiveness in the future.
Unprecedented advancements have been witnessed in deep learning, particularly in the domain of real-world object detection. The sophisticated capabilities of contemporary deep learning methodologies to extract and process features from intricate data have catalyzed their adoption across diverse fields, including computer vision, robotics, and autonomous systems. This comprehensive literature survey examines cutting-edge deep learning approaches employed for effective real-world object detection. The review encompasses various aspects of object identification, such as detection, recognition, and segmentation, while exploring diverse deep learning frameworks, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Deep Belief Networks (DBNs). Furthermore, the survey provides a thorough examination of current research developments, addressing critical challenges and limitations, while identifying promising avenues for future research endeavors. The review offers valuable insights into state-of-the-art techniques and their potential real-world applications.
