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235 articles for “disease detection”
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Crop Disease Prediction by Machine Learning
Abstract: The classification of Crop can be classified into several methods. The data set of crop leaf illnesses, notably Bacterial Leaf Blight disease (BLB), a crop leaf disease with significant outbreaks throughout Thailand, and Brown Spot Crop disease (BSR), is classified employing image classification in this study. Additionally, image processing technology is used for identifying different types of crop leaf disease. These algorithms include the Random Forest, Decision Tree, Gradient Boost, …
Published in Trends in Machine design · Vol. 11, Issue 2, 2024 · pp. 21–25 Read article
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Lung Cancer Detection and Classification Using Deep Learning
Abstract: Lung cancer is a disease that can be effectively treated if detected early. Various technologies, such as magnetic resonance imaging, isotopes, X-rays, and computed tomography scans, are employed for diagnosis. One of the most crucial strategies in combating cancer is early detection, which greatly enhances a patient’s likelihood of survival; this is where artificial intelligence plays a significant role. The approach proposed in this study leverages historical medical data to …
Published in Research and Reviews: Journal of Oncology and Hematology · Vol. 13, Issue 3, 2024 · pp. 11–17 Read article
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Image Preprocessing and Analysis on Eye Fundus Images Segmentation by Using Density Clustering Methods
Abstract: In order to do an automated evaluation of various retinal illnesses such as Diabetic retinopathy, Glaucoma, and Macular Edema, fundus images must be pre-processed first. For many reasons, it's difficult to accurately detect the optic disc. Many blood vessels cross the optic disc, making it difficult to discern the disc's boundaries in fundus images. Lesion regions in diabetic retinopathy look very much like an optic disc's colour and texture, so …
Published in Recent Trends in Sensor Research & Technology Read article
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Face Mask Detection on Real Time Images and Videos using Deep Learning
Abstract: A big change has occurred in our day-to-day lives as a result of COVID-19. One of these changes is the widespread adoption of face masks as a preventative measure against the transmission of the virus. Because of this, face mask detection has developed into an indispensable technique in a variety of contexts, ranging from public areas to industrial settings. Artificial intelligence (AI) and machine learning algorithms are utilized in the …
Published in International Journal of Electrical Machine Analysis and Design · Vol. 2, Issue 1, 2024 · pp. 22–30 Read article
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Classifying Abnormalities in Heartbeat Sound
Abstract: Heartbeat sounds play a major role in the detection of various diseases such as heart disease, hyperthyroidism, and high blood pressure in their early stages. In the proposed method, various abnormal and healthy heartbeat audio signals are given as input and the features are extracted using MFCC (mel-frequency cepstral coefficients). Then, a deep learning approach is applied in which the MFCC audio signals are sent to the CNN (convolutional neural …
Published in Research & Reviews: A Journal of Embedded System & Applications · Vol. 12, Issue 1, 2024 · pp. 24–31 Read article
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From Battlefield to Biodiversity: The Evolution of Drones in Modern Conservation Efforts in Wildlife
Abstract: The rapid advancement of drone technology, encompassing unmanned aerial vehicles (UAVs), unmanned aircraft systems (UAS), and remotely piloted aircraft (RPAs), has significantly impacted various fields, particularly environmental management and wildlife conservation. Originally designed for military use, drones have now become essential tools in ecological research, offering a cost-effective and minimally invasive way to monitor and protect ecosystems. These sophisticated "eco-drones" have revolutionized data collection, especially in hard-to-reach and previously inaccessible …
Published in International Journal on Drones · Vol. 1, Issue 1, 2025 · pp. 8–12 Read article
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The Impact of Climate Change on Infectious Diseases: Potential Risks and Mitigation Strategies
Abstract: As we confront the pressing challenge of climate change, it's vital to recognize its intertwined connection with infectious diseases. We must now navigate a future that combines strong public health interventions, environmental stewardship, and strong policy actions as we stand at the crossroads of crises. In an attempt to lessen the drastic effects of climate change on the dynamics of disease transmission, this study identifies potential risks and creative solutions. …
Published in Research and Reviews : A Journal of Medical Science and Technology · Vol. 13, Issue 2, 2024 · pp. 21–32 Read article
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Leveraging AI and Machine Learning for Early Prediction and Prevention of Non- Communicable Diseases in Resource-Limited Settings
Abstract: Populations in these regions face persistent structural barriers, such as underdeveloped healthcare infrastructure, shortages of trained health professionals, and fragmented or incomplete health information systems. These limitations delay timely diagnosis, restrict access to preventive care, and compromise effective disease management. In recent years, rapid progress in artificial intelligence (AI) and machine learning (ML) has opened promising avenues to mitigate these challenges. Practical applications already emerging include mobile health platforms for …
Published in Journal of AYUSH: Ayurveda, Yoga, Unani, Siddha and Homeopathy · Vol. 15, Issue 1, 2026 · pp. 9–15 Read article
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Predicting Multiple Diseases Using Machine Learning: A Data-Driven Approach
Abstract: The increasing prevalence of chronic and life-threatening diseases highlights the need for innovative healthcare solutions that enable early detection and proactive management. The Multiple Disease Prediction Platform is a web-based system utilizing machine learning (ML) and deep learning (DL) algorithms to analyze user-inputted health data, generating real-time predictions of potential health risks. By leveraging Python’s Streamlit library, the platform provides an interactive and accessible diagnostic experience, eliminating the need for …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 2, 2025 · pp. 16–35 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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Non -Invasive Ways to Detect Cancer
Abstract: Cancer is a diverse group of diseases characterized by uncontrolled cell growth and division, affecting millions worldwide and being a leading cause of death. The disease typically involves genetic alterations that disrupt the normal balance of cell growth, leading to the formation of tumors. Tumors can be benign, posing no threat as they do not spread, or malignant, capable of invading nearby tissues and metastasizing. There are more than 100 …
Published in Research and Reviews : A Journal of Life Sciences · Vol. 14, Issue 2, 2024 · pp. 59–67 Read article
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Uses of Nanotechnology in Medical Diagnostic Applications
Abstract: Nanoparticle and nanodevice is a boon for mankind to treat and deal with disease and medical diagnosis respectively. Nanotechnology is formed by the unity of chemistry, engineering, biology and medicine restricted to the nanoscale (about 1–100 nanometer (nm)). For example, early detection of malignant tumors and cancer biomarkers, which is impossible using conventional technologies. In here we will discuss nanosized sensors to identify different pathological parameters, foreign antigen and toxic …
Published in Research and Reviews : A Journal of Medical Science and Technology Read article
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Comparison of K-nearest Neighbor and Artificial Neural Network Classifiers for the Detection of Breast Cancer
Abstract: Breast cancer is the most common type of cancer seen in women in the present day, which is also considered a life-threatening disease. If this cancer can be detected in its early stage it can be a lifesaver for many people around the world. Machine Learning techniques have become one of the hotspots for predicting the early diagnosis of breast cancer. This research work experiments with the two most popularly …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 11, Issue 1, 2024 · pp. 78–83 Read article
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Effectiveness of structured teaching programme on knowledge regarding ECG and its basic interpretation among second semester BSc Nursing students in selected nursing college at Pathanamthitta district
Abstract: Cardiovascular diseases have now become the leading cause of mortality in India. Cardiovascular disease accounts for twenty five percent of all mortality. Electrocardiogram has an important role in the early detection and treatment of various cardiovascular diseases. Over the year significant advancements have been made in the field of ECG research, leading to the development of new techniques, technologies and algorithms for ECG analysis. ECG allows nurse to diagnose and …
Published in International Journal of Biomedical Innovations and Engineering · Vol. 1, Issue 2, 2023 · pp. 09–20 Read article
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Pneumonia Detection and Classification Using Deep Learning
Abstract: Pneumonia, an infectious lung disease primarily caused by bacteria, often exacerbated by environmental factors, leads to the accumulation of pus in the lung’s alveoli. Accurate diagnosis through chest X-rays, ultrasounds, or lung biopsies is crucial to avoid misdiagnosis and ensure proper treatment, crucial for patients’ quality of life. Diagnostic capacities have been greatly improved by deep learning advances, especially with convolutional neural networks (CNNs). This research presents a robust CNN-based …
Published in Journal of Microwave Engineering and Technologies · Vol. 11, Issue 3, 2024 · pp. 9–19 Read article
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A Systematic Review on Leukemia Detection and Classification Techniques Using Gene Expression
Abstract: Early diagnosis of genetic diseases is crucial for effective treatment, especially in the case of Leukemia, a type of blood cancer characterized by abnormal proliferation of white blood cells. This paper presents a systematic review of recent computational techniques for the detection and classification of Leukemia using gene expression data obtained from DNA microarray analysis. The study explores diverse methodologies including machine learning (ML), deep learning (DL), and bio-inspired algorithms …
Published in International Journal of Genetic Modifications and Recombinations · Vol. 3, Issue 2, 2025 Read article
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A Dual-Model Deep Learning Framework for Early Alzheimer’s Detection Using Clinical Data and Neuroimaging with Architectural Performance Analysis
Abstract: Alzheimer’s disease (AD) poses a significant global health challenge due to its increasing prevalence and the absence of definitive cures. Early diagnosis is crucial for effective intervention and management. This study presents a dual-model deep learning framework for the early detection and classification of AD using both structured clinical data and neuroimaging datasets. Model 1 utilizes a greedy layer-wise autoencoder approach applied to structured data, achieving optimal binary classification accuracy …
Published in Research and Reviews: A Journal of Neuroscience · Vol. 16, Issue 1, 2026 · pp. 1–12 Read article
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Melanoma Skin Cancer Detection Using Deep Learning
Abstract: Cancer as one of the major diseases rank in the World is still very challenging to diagnose and treat hence need for the technological advancements. Chemotherapy, radiation, as well as surgery therapies have several drawbacks including non-selective action, damage to healthy tissues, and multi-drug resistance. Smart nano-theranostics, an advanced integration of nanotechnology with diagnostic and therapeutic modalities, offers a next-generation approach for precision oncology. Thus, the development of multifunctional nanoparticles …
Published in Research and Reviews: Journal of Oncology and Hematology · Vol. 14, Issue 2, 2025 Read article
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Crispr Cas – Revolutionizing Modern Therapies and Beyond
Abstract: CRISPR-Cas technology has emerged as a transformative tool in modern molecular biology, revolutionizing both fundamental research and clinical applications. This RNA-guided gene-editing system enables precise and efficient genomic modifications, offering unprecedented potential for addressing genetic disorders, infectious diseases, and oncological conditions through innovative therapeutic interventions. The inherent specificity and programmability of CRISPR-Cas systems have facilitated breakthroughs in diverse fields, including precision medicine, regenerative therapies, and immuno-oncology. Beyond its therapeutic applications, …
Published in International Journal of Genetic Modifications and Recombinations · Vol. 3, Issue 1, 2025 · pp. 25–38 Read article
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To Assessment of the Effectiveness of a Structured Educational Program on Women's Knowledge of Sexually Transmitted Diseases in Rural Areas of Ludhiana, Punjab: A Quasi-experimental Study
Abstract: Background: Maintaining good health is of immeasurable value, as it empowers individuals to lead socially and economically fruitful lives. However, various disorders have the potential to disrupt one's health. Among these, sexually transmitted diseases (STDs) are the most prevalent disorders affecting the reproductive system and other bodily systems, resulting in significant consequences for both families and society. Sexually transmitted diseases (STDs) can be triggered by various microorganisms, including bacteria (like …
Published in International Journal of Evidence Based Nursing And Practices · Vol. 1, Issue 2, 2023 · pp. 19–26 Read article