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53 articles for “automated diagnostics”
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Automated Crop Disease Detection Using Convolutional Neural Networks
Abstract: Crop diseases contribute to major losses in agricultural production worldwide generating enormous economic costs. This study investigates the possibility of Convolutional Neural Networks (CNN) imaging techniques to auto-detect diseases associated with plants through image processing. A model was developed and trained on a publicly available plant disease dataset containing labeled images of several diseases. The CNN could classify various plant diseases with accuracy of 95%, precision of 92%, and recall …
Published in International Journal of Cheminformatics · Vol. 3, Issue 2, 2025 · pp. 7–15 Read article
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Ai-Driven Healthcare System for Enhanced Diagnosis and Patient Interaction
Abstract: Deep learning techniques are used in an AI-driven healthcare system to improve disease identification and medical picture analysis. Data collection, preprocessing, model training, and evaluation are all part of the system's systematic workflow. Various deep learning architectures, such as ResNet50, VGG-16, and U-Net, are employed for precise classification and segmentation of medical images. The approach incorporates advanced techniques such as optimization, transfer learning, and data augmentation to significantly enhance the …
Published in Research and Reviews: A Journal of Health Professions · Vol. 15, Issue 2, 2025 Read article
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Leveraging Information Technologies (IoT, Sensor Technologies, AI, and Data Analytics) in Healthcare and Agriculture
Abstract: This paper explores the powerful convergence of digital technologies — the Internet of Things (IoT), Sensor Technologies, Artificial Intelligence (AI), and Data Analytics — in transforming healthcare and agriculture. Both sectors face pressing global challenges: rising population demands, environmental stress, disease burdens, unequal access to services, and food insecurity. Conventional systems alone cannot meet future needs. However, technology-driven, real-time data-driven systems offer innovative solutions: from automating diagnostics to forecasting pest …
Published in Journal of Telecommunication, Switching Systems and Networks · Vol. 12, Issue 3, 2025 · pp. 20–28 Read article
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Automated Blood Cell Counting and Disease Identification Using Image Processing: Implications for Polymer Composite- Based Biomedical Diagnostic Devices
Abstract: Accurate quantification of blood cells is central to clinical decision-making and to the performance of emerging polymer composite–based diagnostic platforms. This work presents a cost-effective, image-processing pipeline for automated counting of red blood cells (including overlapping cells), white blood cells, and platelets from Leishman-stained peripheral blood smears, and articulates its relevance to polymer composite microfluidic and biosensor devices. Implemented in Python with OpenCV, the workflow performs grayscale conversion, median/Gaussian denoising, …
Published in Journal of Polymer & Composites · Vol. 13, Issue 6, 2025 · pp. 262–270 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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The Integration of Artificial Intelligence in Ophthalmology: Augmenting Clinical Decision or Clinical Dependency?
Abstract: The diagnostic accuracy of artificial intelligence (AI) algorithms for glaucoma and diabetic retinopathy screening is on par with or higher than that of skilled doctors. Their acceptance has accelerated due to regulatory clearances, practical implementation in telehealth networks, and growing proof of cost-effectiveness. However, this very success raises a question that the field has been reluctant to address: are we unintentionally undermining the clinical reasoning abilities of the upcoming generation …
Published in Research and Reviews: A Journal of Health Professions · Vol. 16, Issue 1, 2026 · pp. 31–34 Read article
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Fuzzy C-Means Clustering for Effective Segmentation and Classification of Brain Tumors in MRI Scans
Abstract: The paper discusses the importance of detecting and classifying brain tumors via MRI for effective treatment. It proposes a framework utilizing the Fuzzy C-means clustering algorithm for segmentation, demonstrating improved performance through real dataset validation. The model is trained on a large, annotated MRI dataset to identify and classify different tumor types, enabling machine learning-based classification into benign and malignant tumors. The MATLAB-based solution automates brain tumor feature extraction, aiding …
Published in Journal of Microwave Engineering and Technologies · Vol. 11, Issue 2, 2024 · pp. 23–28 Read article
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Precision in Progress: The Crucial Nexus of Instrumentation and Measurement in Biotechnology Advancements
Abstract: This comprehensive review examines the critical importance of instrumentation and measurement technologies in enabling advances across diverse domains of modern biotechnology. Detailed examples are provided demonstrating how analytical tools empower quantitative biology, drive discoveries, catalyze innovations, and provide solutions to global challenges. The historical evolution of bioanalytical techniques is traced, highlighting milestones that unleashed new capabilities. Current trends and future outlook are explored, underscoring the ongoing pivotal need for precision …
Published in International Journal of Molecular Biotechnological Research · Vol. 1, Issue 2, 2023 · pp. 11–19 Read article
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Early Lung Cancer Prediction using deep Learning
Abstract: Lung cancer is a global killer because it’s often found late. Finding it early is key to treatment and survival so computer assisted diagnostics are essential. This research uses deep learning to spot early stage lung cancer from CT scans. We trained and fine-tuned three convolutional neural networks—ResNet50, Dense Net 201 and EfficientNet-B0—using transfer learning. We preprocessed the lung CT images by resizing, normalizing and augmenting them to enhance the …
Published in Research and Reviews: Journal of Oncology and Hematology · Vol. 15, Issue 2, 2026 Read article
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U-Net Based Approach for Automated Brain Tumor Classification
Abstract: Brain tumor detection and identification play vital roles in diagnostic procedures in the field of medicine, with the conventional analysis of MRI images requiring a lot of time and also subject to variability. The proposed study involves the use of a CNN-U-Net based approach for brain tumor detection and identification automatically. The study uses a database of 3,064 contrast-enhanced T1-weighted MRI images from 233 patients with the tumors of meningioma, …
Published in Research and Reviews: Journal of Oncology and Hematology · Vol. 14, Issue 2, 2025 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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Intelligent Medical Devices and Robotics in Modern Healthcare: Technological Advancements and Economic Considerations
Abstract: The integration of robots and intelligent medical devices in intensive care units (ICUs) represents a significant advancement in healthcare technology. These systems, including robotic assistants, automated monitoring tools, and AI-powered diagnostic devices, are designed to enhance patient care, streamline workflows, and reduce human error. Robots in the ICU can assist with routine tasks such as medication delivery, patient repositioning, and even basic surgeries, enabling healthcare professionals to focus on critical …
Published in Journal of Advancements in Robotics · Vol. 11, Issue 3, 2024 · pp. 18–27 Read article
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Evaluating the Role of Platelet Indices, with a Focus on Immature Platelet Fraction (IPF), in Differentiating Hyper-Destructive and Hypo-Productive Thrombocytopenia: A Study from Ludhiana, Punjab, India
Abstract: Background: Thrombocytopenia, characterized by a reduction in platelet count, is commonly observed in clinical settings. Its etiology can be broadly classified into hyper-destructive thrombocytopenia, where platelets are destroyed at an accelerated rate, and hypo-productive thrombocytopenia, where platelet production is impaired. Differentiating between these two causes is essential for effective management. The Immature Platelet Fraction (IPF) has emerged as a promising non-invasive diagnostic tool to distinguish these causes. Objectives: The primary …
Published in Research and Reviews: Journal of Oncology and Hematology · Vol. 14, Issue 2, 2025 Read article
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A Study on Automatic Feedback Control by Image Processing for Mixing Solutions in a Microfluidic Device
Abstract: Precise and rapid mixing of chemical and biological reagents is a critical yet challenging aspect of microfluidic systems, often limited by laminar flow conditions and the need for manual or pre- programmed interventions. Existing mixing strategies frequently lack real-time adaptability and closed-loop control, hindering reproducibility and the execution of complex reaction protocols. This work presents an innovative automatic feedback control system utilizing the study on real- time image processing to …
Published in International Journal of Advanced Control and System Engineering · Vol. 3, Issue 2, 2025 · pp. 32–41 Read article
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Artificial Intelligence in Diagnostics: Advancements, Challenges, and Future Prospects
Abstract: AI is changing (and will change) healthcare as we know it, and diagnostics might be the specialty that feels the most discomfort. Artificial intelligence-based analytical systems are facilitating the detection, diagnosis, and treatment of a variety of diseases, with better accuracy, speed, and results. Now, this abstract investigates the role of AI in diagnostics, scouring its elements, landmark techniques, transformative impact and future overview. This article explains AI and discusses …
Published in Research and Reviews: A Journal of Health Professions · Vol. 15, Issue 3, 2025 · pp. 8–17 Read article
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Intelligent Systems: A study on AI and Machine learning
Abstract: Artificial Intelligence (AI) and Machine Learning (ML) are dynamic branches of computer science that focus on developing systems capable of executing tasks commonly associated with human intelligence. These activities encompass making choices, resolving issues, understanding language, identifying patterns, and learning through experience. Artificial Intelligence refers to the broad area of designing systems and frameworks that enable machines to perform tasks resembling human thought and behavior. This field integrates diverse technologies …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 13, Issue 1, 2025 Read article
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Skin Disease prediction and classification from dermoscopy images using Neural Network
Abstract: Skin diseases are among the most common health-related problems affecting people of all age groups, and their occurrence often varies with seasonal and environmental conditions. Delayed or incorrect diagnosis of skin disorders can lead to severe complications, making early and accurate detection extremely important for effective treatment and prevention. In recent years, rapid advancements in deep learning and neural network technologies have significantly contributed to the development of automated medical …
Published in Journal of Computer Technology & Applications · Vol. 17, Issue 1, 2026 Read article
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Classification of Skin Disease Images Using EfficientNetTransfer Learning Technique
Abstract: Melanomous Skin lesions are among the deadliest kinds of cancer. Despite being a very uncommon skin condition, melanoma is responsible for 75% of cancer-related deaths. Dermatologists perform dermoscopy more commonly than any other procedure. Since it aggravates the skin condition, a dermatologist will be the first to identify it during an inspection. Because this strategy depends on the user's visual perception and experience, it can only be used by highly …
Published in Journal of Telecommunication, Switching Systems and Networks · Vol. 11, Issue 1, 2024 · pp. 7–12 Read article
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Interpretable Skin Cancer Detection via Optimized CNN Models for Smart Healthcare Solutions
Abstract: Skin cancer is a common and potentially life-threatening condition, highlighting the importance of reliable and efficient diagnostic techniques. Recently, convolutional neural networks (CNNs) have demonstrated significant potential in automating the classification of skin cancer using thermoscopic images. Despite these advancements, the lack of interpretability in these models poses a barrier to their widespread use in clinical settings. In this study, we propose an interpretable CNN architecture optimized for skin cancer …
Published in Research & Reviews: A Journal of Bioinformatics · Vol. 12, Issue 1, 2025 · pp. 41–45 Read article
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A Literature Review on Internet of Medical Things
Abstract: Artificial Intelligence (AI) is transforming healthcare by improving diagnostics, treatment planning, and patient management through data-driven insights and automation. The Internet of Medical Things (IoMT) represents a significant shift in modern healthcare, enabling real-time patient monitoring, data-driven decision-making, and enhanced medical outcomes. This literature review explores the architecture of IoMT, including perception layer, network layer, transport layer and application layer. It also thoroughly explores key challenges like ensuring data security, …
Published in International Journal of Wireless Security and Networks · Vol. 3, Issue 2, 2025 · pp. 23–34 Read article