Breast Cancer Classification using XGBoost

World Journal of Advanced Research and Reviews, Год журнала: 2024, Номер 21(2), С. 1985 - 1994

Опубликована: Фев. 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.

Язык: Английский

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Heart Disease Prediction using SVM

International Journal of Science and Research Archive, Год журнала: 2024, Номер 11(2), С. 412 - 420

Опубликована: Март 18, 2024

Diagnosing and predicting the outcome of cardiovascular disease are essential tasks in medicine that help ensure patients receive accurate classification treatment from cardiologists. The use machine learning healthcare sector has grown due to its ability identify patterns data. By applying techniques classify presence diseases, it's possible decrease rate misdiagnosis. This study aims create a model capable accurately forecasting diseases minimize deaths associated with these conditions. In this paper, two types SVM such as linear polynomial is used. Accuracy, precision, recall F1 score been evaluated for comparing SVM. Polynomial provides better accuracy than

Язык: Английский

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Mechanical characterization of materials using advanced microscopy techniques

World Journal of Advanced Research and Reviews, Год журнала: 2024, Номер 21(3), С. 274 - 283

Опубликована: Март 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.

Язык: Английский

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Pneumonia prediction using deep learning in chest X-ray Images

International Journal of Science and Research Archive, Год журнала: 2024, Номер 12(1), С. 767 - 773

Опубликована: Май 24, 2024

Pneumonia, a potentially fatal lung disease caused by viral or bacterial infection, poses challenges in diagnosis from chest X-ray images due to similarities with other infections. This research aims develop computer-aided system for pneumonia detection children, enhancing diagnostic accuracy. In this paper, five established deep learning models such as VGG-16, VGG-19, ResNet-50, Inception-V3, Xception pre-trained on ImageNet have been used. These applied the dataset optimize performance. provides recall, specificity, accuracy and AUC of 97.43%, 91.02%, 95.06% 94.23%, respectively.

Язык: Английский

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Empowering blockchain with SmartNIC: Enhancing performance, security, and scalability

World Journal of Advanced Research and Reviews, Год журнала: 2024, Номер 22(1), С. 151 - 162

Опубликована: Апрель 7, 2024

This paper introduces BlockNIC, an innovative blockchain infrastructure designed to operate exclusively on SmartNICs. Unlike traditional implementations, BlockNIC leverages the unique capabilities of SmartNICs execute relatively simple computations directly network path, eliminating need for additional hardware and reducing reliance host CPUs. By harnessing idle resources within network, significantly reduces energy consumption requirements, addressing environmental concerns associated with conventional architectures. Through comprehensive performance comparisons between bare-metal servers, this study demonstrates promising potential in achieving scalability, security, sustainability networks. The findings highlight BlockNIC's ability enhance overall reliability while minimizing resource limitations, thereby unlocking new possibilities various applications use cases previously hindered by constraints. emergence aligns global agenda, offering a timely solution challenges posed technologies. promoting adoption SmartNIC-based infrastructures, research contributes greener more secure digital future. It emphasizes importance exploring approaches address impact technological innovations, urging researchers, industry professionals, policymakers recognize transformative solutions advancing efficiency ecosystems.

Язык: Английский

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Hepatitis C prediction using SVM, logistic regression and decision tree

World Journal of Advanced Research and Reviews, Год журнала: 2024, Номер 22(2), С. 926 - 936

Опубликована: Май 16, 2024

Hepatitis C is an infection of the liver brought on by HCV virus. In this condition, early diagnosis challenging because delayed onset symptoms. Predicting well enough can spare patients from permeant damage. The primary goal work to use several machine learning methods forecast disease based widely available and reasonably priced blood test data in order diagnose treat on. Three techniques support vector (SVM), logistic regression, decision tree, has been applied one dataset work. To find a suitable approach for illness prediction, confusion matrix, precision, recall, F1 score, accuracy, receiver operating characteristics (ROC), performances different strategies have assessed. SVM model's overall accuracy 0.92, highest among three models.

Язык: Английский

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Explainable Detection and Analysis of Cauliflower Leaf Diseases

Lecture notes in computer science, Год журнала: 2025, Номер unknown, С. 165 - 182

Опубликована: Янв. 1, 2025

Язык: Английский

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Improved performance on melanoma skin cancer classification using deep learning based ensemble technique

Intelligent Data Analysis, Год журнала: 2025, Номер unknown

Опубликована: Март 11, 2025

Skin cancer, particularly melanoma, arises from DNA damage that leads to abnormal cell growth in the epidermis. Early detection is crucial as melanoma can spread rapidly, but it highly curable if identified promptly. Detecting and diagnosing early are essential reduce mortality rates associated with this type of cancer. In literature, various ensemble techniques have been proposed improve performance. This paper introduces a deep learning based method aimed at enhancing accuracy skin cancer detection. Additionally, presents thorough performance evaluation five techniques. Initially, dataset underwent pre-processing, involving removal artifacts through hair removal, achieving balance distribution images for each class image augmentation Then, architecture 16 pre-trained models was modified by adding additional layers their The achieved highest were selected ensembling. Since VGG16, MobileNetV2, DenseNet169 accuracy, they chosen Five techniques, namely, weighted average, voting, bagging, boosting, stacking, applied architectures fine-tuned such classify images. experiments performed on combined HAM10000 ISIC2019, which contains seven lesion classes. results demonstrate average model achieves overall 81.99% classification 89.85%. positive outcomes affirm employing adjusted enhances performance, thereby demonstrating potential utility

Язык: Английский

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A comprehensive review on step-based skin cancer detection using machine learning and deep learning methods

Archives of Computational Methods in Engineering, Год журнала: 2025, Номер unknown

Опубликована: Март 19, 2025

Язык: Английский

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A Method for Detecting Skin Cancer Disease Based on Deep Learning in Dermoscopic Images

Journal of Computer and Communications, Год журнала: 2025, Номер 13(03), С. 138 - 155

Опубликована: Янв. 1, 2025

Язык: Английский

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