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305 articles for “image pre-processing”
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Intelligent Brain Tumor Diagnosis with AI-Based Classification* * Harnessing Deep and Machine Learning for Tumor Identification
Abstract: Brain tumors have become a leading cause of cancer- related deaths, posing significant health risks to many patients. This urgent medical challenge calls for rapid, automated, and reliable techniques to detect brain tumors accurately. Timely and precise tumor identification is crucial for devising effective medical plans that have the potential to save lives and improve patient outcomes. By leveraging advanced image processing methods, healthcare professionals can enhance their diagnostic capabilities …
Published in International Journal of Cell Biology and Cellular Functions · Vol. 4, Issue 1, 2026 Read article
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Image Enhancement Techniques for Digital Images
Abstract: In image processing, image enhancement is a challenging problem. Image enhancement aims to process an image in a way that makes the final product better suited for a particular application than the original. There are numerous ways to improve the quality of images using digital image enhancement techniques. It is crucial to use these techniques correctly. Image enhancement is a critical aspect of image processing, focusing on improving the visual …
Published in International Journal of Radio Frequency Innovations · Vol. 3, Issue 1, 2025 · pp. 14–18 Read article
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Animal Species Prediction Using Deep Learning
Abstract: In the face of escalating biodiversity loss, effective monitoring of animal species is critical for conservation efforts. This study presents a deep learning approach for species detection and a multimodal feature identification technique for animals vulnerable to poaching. The suggested prediction system recognizes objects automatically by the application of deep learning techniques to detect objects and then recognize them by using computer vision techniques, and it is triggered when an …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 13, Issue 1, 2026 · pp. 14–22 Read article
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Multimodal Disease Detection Using Deep Learning
Abstract: Artificial Intelligence (AI) is playing an increasingly pivotal role in modern healthcare, particularly in improving the speed and accuracy of disease detection. With the evolution of Machine Learning (ML), Deep Learning (DL), and high-performance computing, AI-based solutions are now capable of processing extensive medical datasets, ranging from patient records to diagnostic images, with remarkable efficiency. These systems offer immense potential for early intervention, improved clinical decision-making, and alleviating pressure on …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 2, 2025 · pp. 129–139 Read article
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Advancements in AI-Driven Diagnostics for Dental Health: A Comprehensive Review
Abstract: Dental diseases, also known as oral diseases or dental conditions, encompass a range of health problems affecting the teeth, gums, mouth, and associated structures. These conditions can lead to pain, discomfort, and severe complications if left untreated. Early detection and accurate diagnosis are crucial for effective treatment and prevention of further complications. This comprehensive literature review aims to identify common dental problems such as Tooth Decay (Cavities), Gingivitis, Periodontitis, and …
Published in Current Trends in Signal Processing · Vol. 14, Issue 2, 2024 · pp. 1–7 Read article
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Efficient AES for Biometric Image Signal Processing
Abstract: Abstract: Several computation schemes and architectural designs have been suggested during the last three decades for efficient hardware implementation of biometric applications. The biometric application of advanced encryption standard (AES) algorithm in high texture image signal processing. The proposed AES very large-scale Integration (VLSI) architecture has 16 % and 15 % less dynamic power consumption and LUT utilization then available best design available AES architecture in the presented literature survey. …
Published in Current Trends in Signal Processing · Vol. 9, Issue 1, 2019 · pp. 7–9 Read article
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Comparative Analysis of Supervised Learning Algorithms
Abstract: Supervised learning is a fundamental and widely used branch of machine learning in which models are trained on labeled datasets, meaning that each input is associated with a known output. Supervised learning algorithms develop predictive capability by understanding the mapping between input variables and corresponding output labels, enabling them to accurately forecast outcomes for previously unseen data. Due to this capability, supervised learning has found extensive applications across diverse domains …
Published in Journal of Computer Technology & Applications · Vol. 17, Issue 1, 2026 · pp. 25–30 Read article
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Thermo-Responsive Polymer Blends for Minimally Invasive Medical Procedures
Abstract: Thermo-responsive polymer blends have become a key new class of biomaterials for the minimally invasive medical procedure. They offer precision, flexibility and greater patient safety. These smart materials undergo a change of physical/ mechanical properties relative to temperature difference in the body, limiting tissue trauma and increasing effectiveness of the procedure. Their adjustable sol-gel behavior, shape-memory effects and reversible volume change provides an opportunity for their precise delivery through catheters, …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
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A Comparative Machine Learning Framework for Early Prediction of Liver Cancer Using Clinical Attributes
Abstract: One of the main causes of cancer-related death globally is liver cancer, and improving patient outcomes depends heavily on early detection. However, low contrast, noise, organ similarity, and tumor shape and size variability make it difficult to accurately identify and segment liver tumors from medical imaging. Automated liver cancer diagnosis, segmentation, and prognosis have been greatly improved by recent developments in artificial intelligence (AI), especially deep learning. This work presents …
Published in Research and Reviews: Journal of Oncology and Hematology · Vol. 15, Issue 2, 2026 · pp. 39–47 Read article
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Virtual Method to Predict Dental Disease
Abstract: The integration of technology and medicine in the healthcare domain has led to the emergence of inventive strategies to improve patient care and diagnostics. One such groundbreaking methodology is the utilization of Convolutional Neural Networks (CNNs) within the domain of deep learning, particularly for image recognition and processing tasks. In this paper, we propose a novel approach to image recognition that employs state-of-the-art deep learning algorithms to create a user-friendly …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 11, Issue 1, 2024 · pp. 8–15 Read article
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Autonomous Infection Control System
Abstract: Escalating problems arise mainly at hospitals due to transmission of microbes through air, which causes life threatening situations. Doctors, nurses and other members are more vulnerable to diseases from within the place. PPE kits and other disinfecting procedures require an in-human presence to do the work, which makes it more challenging and puts the person's life at risk. In current scenarios, human presence is required for sanitising and cleaning procedures, …
Published in Journal of Mechatronics and Automation · Vol. 11, Issue 2, 2024 · pp. 12–18 Read article
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Driver Drowsiness Detection using Face Monitoring and Pressure Measurement
Abstract: Fatigue driving is the driver, after long periods of continuous driving, experiences mental and physical functional disorder. The international statistics shows that it is one of the major causes of accidents in the world. Detecting the drowsiness of the driver can issue timely warning that could help in preventing many accidents. In this research a system is designed to detect the drowsiness of a driver through face monitoring techniques and …
Published in Research & Reviews: A Journal of Embedded System & Applications · Vol. 5, Issue 3, 2017 · pp. 12–18 Read article
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Scaling of Machine Learning Techniques in Medical Imagining and Biomedical Applications Concerning Healthcare
Abstract: Machine learning refers to a field within computer science enabling computers to learn without explicit programming. Stemming from artificial intelligence's study of pattern recognition and computational learning theory, machine learning develops algorithms capable of learning from vast datasets and making predictions. Its applications span diverse computing tasks like email filtering, network intrusion detection, optical character recognition, and computer vision, where conventional algorithm design proves challenging. Notably, in computer vision, a …
Published in Journal of Computer Technology & Applications · Vol. 15, Issue 1, 2024 · pp. 41–44 Read article
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Human Behaviour Anomaly Tracking System for Metro Railways
Abstract: Increasing suicidal attempts at metro railway stations (underground railways) in today’s world is a very serious matter of concern. The aim of this idea is to utilize the advancement of technology for saving precious human lives. An intelligent system can be designed which uses real time Image Processing/Computer Vision/Deep Learning to reduce suicidal attempts and unusual behaviour (viz. planning for sabotage etc.) at underground Metro Station. The vision system constantly …
Published in Journal of Control & Instrumentation · Vol. 10, Issue 1, 2019 · pp. 12–15 Read article
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Vein Detector using Image Processing
Abstract: Abstract: Most of the doctors face problem while giving dose of medicine using syringe to patients who have darker skin tone, have wrinkled skin, small children’s and people who have scorch marks or burned skin. Due to such problems, doctors may insert the syringe in wrong place or it may become harder for doctors to find veins of such patients. This Paper presents a new methodology of Vein Finder Which …
Published in Recent Trends in Electronics Communication Systems · Vol. 6, Issue 1, 2019 · pp. 31–34 Read article
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Leveraging Deep Learning for Accurate Weed Identification
Abstract: Weed control is very important for all types of agricultural businesses. The project here revolves around the application of computer vision techniques and, more concretely, deep learning techniques, for the effective recognition and classification of weeds. The EfficientNetB4 architecture is an appropriate backbone as its scalability and performance optimization is adequate. The modifier used is Adam optimization algorithm which will serve as a pre- processor for the model. Weeds at …
Published in Research & Reviews : Journal of Agricultural Science and Technology · Vol. 14, Issue 2, 2025 · pp. 90–99 Read article
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Boundary Localization of Optic Disc using Pixel Level Deviation in Retinal Fundus Image
Abstract: In automated computer-aided diagnosis (CAD) approach for Diabetic retinopathy (DR) detection and monitoring, optic disc (OD) localization is the first and fundamental task for further processing of retinal fundus image. Identification of OD area is complex, as it attains the similar pathological blood vessels characteristics such as intensity variations and texture pattern. This paper presents a simple approach for the outer boundary localization of OD based on pixel level variations, …
Published in Research and Reviews: A Journal of Health Professions · Vol. 8, Issue 2, 2018 · pp. 14–18 Read article
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Machine Learning Based Early Cataract Detection: A Predictive Modeling Approach
Abstract: Cataracts, characterized by dense cloudy areas in the eye’s lens, afflict more than 50% of elderly individuals, leading to impaired vision and potential blindness. Detecting cataracts at an early stage is crucial to facilitate simpler treatments, as neglecting the condition may necessitate complex eye surgery. To address this issue, we are creating a predictive system that identifies cataract disease by analyzing user-provided eye features. To achieve this, we leverage OpenCV, …
Published in International Journal of Computer Science Languages · Vol. 1, Issue 2, 2023 · pp. 1–8 Read article
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Path Lab-AI: An Autonomous Framework for Error-Free Histopathology Slide Interpretation
Abstract: Path Lab-AI represents a fully autonomous platform for the analysis of histopathology slides with circumscribed structures, designed to obtain highly accurate results using diagnostic methods and avoiding the usual limitations of standard microscopy-based pathology. Leveraging recent deep learning and whole slide image (WSI) analysis innovations, our system takes advantage of automated WSI ingestion along with pre-processing steps to account for staining variability, remove artifacts, and localize tissue from background. Such …
Published in Research & Reviews: A Journal of Bioinformatics · Vol. 13, Issue 1, 2026 · pp. 19–30 Read article
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Cardiovascular Image Segmentation in Computed Tomography Angiography ImagesUsing Deep Learning Approaches
Abstract: In present time, the cardiovascular disease is one of the common causes of mortality in human. In field of medical science, Heart angiography is one of the processes to testing of heart disease. Heart angiography identifies the abnormality in heart vessels. There are mainly two approaches to identify the heart disease. Former approach is the invasive and latter one is the non-invasive approaches. Invasive process is a painful diagnostic procedure …
Published in Recent Trends in Electronics Communication Systems · Vol. 10, Issue 1, 2023 · pp. 20–27 Read article