Breast Cancer Classification using XGBoost

World Journal of Advanced Research and Reviews, Journal Year: 2024, Volume and Issue: 21(2), P. 1985 - 1994

Published: Feb. 28, 2024

Breast cancer continues to be one of the foremost illnesses that results in deaths numerous women each year. Among female population, approximately 8% are diagnosed with (BC), following Lung Cancer. The alarming rise fatality rates can attributed breast being second leading cause. manifests through genetic transformations, persistent pain, alterations size, color (redness), and texture breast's skin. Pathologists rely on classification identify a specific targeted prognosis, achieved binary (normal/abnormal). Artificial intelligence (AI) has been employed diagnose tumors swiftly accurately at an early stage. This study employs Extreme Gradient Boosting (XGBoost) machine learning technique for detection analysis cancer. XGBoost provides accuracy 94.74% recall 95.24% Wisconsin (diagnostic) dataset.

Language: Английский

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Skin cancer classification using NASNet

International Journal of Science and Research Archive, Journal Year: 2024, Volume and Issue: 11(1), P. 775 - 785

Published: Jan. 30, 2024

The importance of making an early diagnosis in both the prevention and treatment skin cancer cannot be overstated. A very effective medical decision support system that can classify lesions based on dermoscopic pictures is essential instrument for determining prognosis cancer. In spite fine-grained variation way different types appear, Deep Convolutional Neural Networks (DCNN) have made great strides recent years toward improving ability to detect using images. It has been claimed there are a few machine learning techniques accurate photos. good number these methods predicated convolutional neural networks (CNNs) already trained, which makes it possible train models only small quantity available training data. However, because so sample images malignant tumors available, classification accuracy still typically severely restricted. primary purpose this study construct DCNN-based model capable automatically classifying as either melanoma or non-melanoma with high level accuracy. We propose optimized NASNet architecture, enhanced additional data basic layer employed CNN added. strategy proposed enhances model's capacity deal incomplete inconsistent dataset 2637 used demonstrate benefits technique proposed. analyze performance suggested method by looking at its precision, sensitivity, specificity, F1-score, area under ROC curve. Optimized Mobile Large provides 85.62% 83.98%, respectively Adam optimizer.

Language: Английский

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Skin cancer classification using Inception Network

World Journal of Advanced Research and Reviews, Journal Year: 2024, Volume and Issue: 21(2), P. 839 - 849

Published: Feb. 15, 2024

Since skin disease is a universally recognized condition among humans, there has been growing interest in utilizing intelligent systems to classify various ailments. This line of research deep learning holds immense significance for dermatologists. However, accurately determining the presence formidable task due intricate nature texture and visual similarities between different diseases. To address this challenge, images undergo filtration eliminate unwanted noise further processing enhance overall quality image. The primary purpose study construct neural network-based model that capable automatically classifying several types cancer as either melanoma or non-melanoma with prominent level accuracy. We propose an optimized Inception architecture, which InceptionNet enhanced data augmentation basic layers. strategy proposed enhances model's capacity deal incomplete inconsistent data. A dataset 2637 are used demonstrate benefits technique proposed. analyze performance suggested method by looking at its precision, sensitivity, specificity, F1-score, area under ROC curve. Proposed provides accuracy 84.39% 85.94%, respectively Adam Nadam optimizer. training process each subsequent layer exhibits notable enhancement effectiveness. An examination inquiry can assist experts making early diagnoses, thereby providing them insight into infection enabling initiate necessary treatment, if deemed necessary.

Language: Английский

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Enhanced MRI-based brain tumor classification with a novel Pix2pix generative adversarial network augmentation framework

Brain Communications, Journal Year: 2024, Volume and Issue: 6(6)

Published: Jan. 1, 2024

Abstract The scarcity of medical imaging datasets and privacy concerns pose significant challenges in artificial intelligence-based disease prediction. This poses major to patient confidentiality as there are now tools capable extracting information by merely analysing patient’s data. To address this, we propose the use synthetic data generated generative adversarial networks a solution. Our study pioneers utilisation novel Pix2Pix network model, specifically ‘image-to-image translation with conditional networks,’ generate for brain tumour classification. We focus on classifying four types: glioma, meningioma, pituitary healthy. introduce deep convolutional neural architecture, developed from architectures, process pre-processed original obtained Kaggle repository. evaluation metrics demonstrate model's high performance images, achieving an accuracy 86%. Comparative analysis state-of-the-art models such Residual Network50, Visual Geometry Group 16, 19 InceptionV3 highlights superior our model detection, diagnosis findings underscore efficacy augmentation technique creating accurate classification, offering promising avenue improved prediction treatment planning.

Language: Английский

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Uncovering COVID-19 conversations: Twitter insights and trends

World Journal of Advanced Research and Reviews, Journal Year: 2024, Volume and Issue: 21(1), P. 836 - 842

Published: Jan. 15, 2024

In this paper, we delve into the public discourse surrounding COVID-19 on Twitter to unearth collective sentiments, concerns, and spread of information during pandemic. By leveraging a dataset relevant tweets corresponding ISO country codes, our analysis will map out geographical digital landscape these conversations. The significance work lies in its potential inform health strategies, shape policymaking, contribute social research crisis communication. Stakeholders ranging from officials have vested interest understanding contours dialogue. Our objective is craft data-driven narrative through visualizations that reveal how world engages with pandemic front, providing actionable insights global local responses using Machine Learning techniques.

Language: Английский

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Utilizing remote sensing data and ArcGIS for advanced computational analysis in land surface temperature modeling and land use property characterization

World Journal of Advanced Research and Reviews, Journal Year: 2024, Volume and Issue: 21(1), P. 1496 - 1507

Published: Jan. 20, 2024

This paper provides a summary of the remote sensing analysis conducted, which utilized satellite images to model changes in land cover and their influence on Land Surface Temperature (LST). The primary determinant surface overheating is identified as vegetation, with water bodies playing significant role LST regulation. Conversely, areas bare soil built-up infrastructure contribute elevated levels. Therefore, it emphasizes importance implementing measures like urban forestry, creating bodies, preserving existing ponds, minimizing construction activities prevent further increases mitigate ecological damage. Even cases where tree planting isn't feasible, introducing shrub-type vegetation barren recommended an effective means resist heat buildup. Consequently, increasing highlighted crucial factor controlling within both non-urban environments.

Language: Английский

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Bearing fault detection by using graph autoencoder and ensemble learning

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: March 3, 2024

Abstract The research and application of bearing fault diagnosis techniques are crucial for enhancing equipment reliability, extending lifespan, reducing maintenance expenses. Nevertheless, most existing methods encounter challenges in discriminating between signals from machines operating under normal faulty conditions, leading to unstable detection results. To tackle this issue, the present study proposes a novel approach based on graph neural networks ensemble learning. Our key contribution is stochasticity-based compositional method that transforms Euclidean-structured data into format processing by networks, with feature fusion newly proposed learning strategy outlier specifically designed diagnosis. This marks significant advancement accurately identifying faults, highlighting our study's pivotal role diagnostic methodologies.

Language: Английский

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Mechanical characterization of materials using advanced microscopy techniques

World Journal of Advanced Research and Reviews, Journal Year: 2024, Volume and Issue: 21(3), P. 274 - 283

Published: March 6, 2024

This review explores the synergistic relationship between advanced microscopy techniques and mechanical engineering, outlining their profound impact on materials science system design. We delve into multifaceted applications of electron microscopy, X-ray diffraction, spectroscopic methods in understanding microstructural dynamics, properties, failure mechanisms integral to engineering. Through a comprehensive synthesis recent research, we emphasize pivotal role these play optimizing material performance, bolstering structural integrity, driving innovation By elucidating intricate details behavior at microscale, contributes informed decision-making selection design processes. Furthermore, address emerging trends prospects, underscoring continued synergy collaboration remains forefront technology, promising ongoing advancements that will shape future landscape innovation.

Language: Английский

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Exploring the versatility of medical textiles: Applications in implantable and non-implantable medical textiles

World Journal of Advanced Research and Reviews, Journal Year: 2024, Volume and Issue: 21(1), P. 603 - 615

Published: Jan. 11, 2024

In the contemporary era, realm of medical textiles stands out as a continuously expanding sector within technical textile market. Essential characteristics encompass factors such robustness, eco-friendliness, safety, compatibility with human body, dimensional reliability, resilience against allergens and cancer, enhanced comfort, efficient antifungal antimicrobial properties. Advances in textiles, whether natural or synthetic, are primarily directed toward improving user comfort. Notably, progress signifies significant stride making challenging days for patients more comfortable. Implantable materials play crucial role addressing diverse needs by restoring affected parts body. These substances utilized fields, including manufacturing wound sutures, surgical substitutes, development artificial ligaments vascular grafts. This range encompasses wide array apparatus implants soft tissues, orthopedic devices, cardiovascular implants. The industry, especially medical, hygiene, healthcare sectors, is witnessing remarkable growing trend. Textiles, due to their versatility product design, have become compelling solution implantable devices. Textiles possess adopt both two-dimensional three-dimensional shapes, driven solely imaginative ideas. realm, utility spans from basic single-thread sutures intricate composite formations employed bone replacement. They serve purposes ranging fundamental cleaning wipes sophisticated barrier fabrics settings. research seeks investigate medical-grade used implants, synthetic skin, ligaments, cartilage replacements. delves into raw these examines processes involved creating vital components field.

Language: Английский

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Decoding COVID-19 Conversations with Visualization: Twitter Analytics and Emerging Trends

Journal of Computer Science Engineering and Software Testing, Journal Year: 2024, Volume and Issue: 10(1), P. 21 - 31

Published: Jan. 1, 2024

This study delves into the vast landscape of COVID-19 discussions on Twitter, aiming to unveil pertinent insights and emerging trends within this dynamic social media platform. Analyzing a substantial volume Twitter data related pandemic, our research scrutinizes content, sentiments, patterns conversations among users. By employing advanced analytics, we discern key themes, prevalent evolution discourse over time. investigation not only provides comprehensive overview diverse topics encompassed but also sheds light shaping public opinion awareness. The abstract influencers amplifiers virtual discourse, identifying pivotal accounts trending hashtags that significantly contribute dissemination information. Moreover, investigates geographical temporal variations in discussions, offering nuanced understanding how these evolve across different regions timeframes. As plays an increasingly central role perceptions, aims valuable for policymakers, health organizations, comprehend dynamics communication Twitter. Ultimately, by uncovering endeavours enhance surrounding pandemic its implications strategies.

Language: Английский

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