Brain Tumour Detection using Deep Learning Technique
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
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
TheRecentadvancementsinmedicalimaging technology, such as integrating 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.
Diabetic foot ulcers (DFUs) represent a major health issue in India, affecting a significant portion of diabetic patients. If not promptly addressed, these ulcers can lead to severe complications such as infection, gangrene, amputation, and chronic wounds. With 72% of DFU patients testing positive for multidrug-resistant organisms (MDROs), the risk of complications is notably elevated. Early detection is crucial for preventing these severe outcomes. This prototype employs artificial intelligence (AI) and deep learning techniques, specifically Convolutional Neural Networks (CNNs), to detect and assess DFUs. By analysing annotated medical images, the system precisely measures the size and depth of ulcers using CNNs. AI facilitates early diagnosis, allowing for timely and tailored treatments, which enhances clinical decision-making and reduces the risks associated with advanced DFUs. The system utilizes 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 contours of the wound 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 effective DFU management.
In this era of digital transformation, image processing techniques play a crucial role in various applications, from security to entertainment. This article 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. On the other hand, 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 article delves into the principles, methodologies, and applications of these cutting-edge techniques, shedding light on their potential impact on the future of image processing.
Recent years have witnessed unprecedented advancements in deep learning, particularly in the domain of real-world object detection. The sophisticated capability of contemporary deep learning methodologies to extract and process features from intricate data has 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 dimensions of object identification, such as detection, recognition, and segmentation, while delving into diverse deep learning frameworks, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Deep Belief Networks (DBNs). Furthermore, the survey offers a thorough examination of current research developments, addressing critical challenges and constraints, while identifying promising avenues for future research endeavors. The review delivers valuable insights into state-of-the-art techniques and their potential real-world applications.
