Published: Sept. 18, 2024
Language: Английский
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
Citations
28
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
Citations
17
International Journal of Science and Research Archive, Journal Year: 2024, Volume and Issue: 11(2), P. 412 - 420
Published: March 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
Language: Английский
Citations
16
International Journal of Science and Research Archive, Journal Year: 2024, Volume and Issue: 12(1), P. 767 - 773
Published: May 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.
Language: Английский
Citations
9
BMC Medical Imaging, Journal Year: 2024, Volume and Issue: 24(1)
Published: Aug. 2, 2024
Abstract Skin cancer stands as one of the foremost challenges in oncology, with its early detection being crucial for successful treatment outcomes. Traditional diagnostic methods depend on dermatologist expertise, creating a need more reliable, automated tools. This study explores deep learning, particularly Convolutional Neural Networks (CNNs), to enhance accuracy and efficiency skin diagnosis. Leveraging HAM10000 dataset, comprehensive collection dermatoscopic images encompassing diverse range lesions, this introduces sophisticated CNN model tailored nuanced task lesion classification. The model’s architecture is intricately designed multiple convolutional, pooling, dense layers, aimed at capturing complex visual features lesions. To address challenge class imbalance within an innovative data augmentation strategy employed, ensuring balanced representation each category during training. Furthermore, optimized layer configuration augmentation, significantly boosting precision detection. learning process using Adam optimizer, parameters fine-tuned over 50 epochs batch size 128 ability discern subtle patterns image data. A Model Checkpoint callback ensures preservation best iteration future use. proposed demonstrates 97.78% notable 97.9%, recall F2 score 97.8%, underscoring potential robust tool classification cancer, thereby supporting clinical decision-making contributing improved patient outcomes dermatology.
Language: Английский
Citations
9
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
Citations
8
World Journal of Advanced Research and Reviews, Journal Year: 2024, Volume and Issue: 22(1), P. 151 - 162
Published: April 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.
Language: Английский
Citations
8
World Journal of Advanced Research and Reviews, Journal Year: 2024, Volume and Issue: 21(2), P. 2007 - 2018
Published: Feb. 28, 2024
Accurate prediction of accident risks plays a crucial role in proactively implementing safety measures and allocating resources effectively. This paper introduces an innovative approach aimed at improving risk by harnessing unique data sources extracting insights from diverse yet sparse datasets. Traditional models often face limitations due to lack diversity scope the available data, which hinders their predictive capabilities. In response this challenge, our study integrates broad spectrum heterogeneous encompassing traffic flow, weather conditions, road infrastructure details, historical records. To overcome difficulties associated with we employ advanced science techniques such as feature engineering, imputation, machine learning. The novel dataset that amalgamates various types, establishing robust foundation for model. Through meticulous analysis, derive valuable these sources, significantly enhancing ability assess risks. proposed offers numerous advantages, including capacity predict accidents areas were previously underrepresented under varying conditions. We rigorously evaluate model's performance through extensive experimentation validate its accuracy using real-world data. Our results indicate substantial improvements compared conventional models. research contributes field highlighting potential benefits integrating leveraging techniques. underscores importance tapping into concealed patterns promote optimize resource allocation accident-prone regions, fostering more secure environments.
Language: Английский
Citations
4
Journal of Machine and Computing, Journal Year: 2025, Volume and Issue: unknown, P. 140 - 153
Published: Jan. 3, 2025
The rapid increase in population density has posed significant challenges to medical sciences the auto-detection of various diseases. Intelligent systems play a crucial role assisting professionals with early disease detection and providing consistent treatment, ultimately reducing mortality rates. Skin-related diseases, particularly those that can become severe if not detected early, require timely identification expedite diagnosis improve patient outcomes. This paper proposes transfer learning-based ensemble deep learning model for diagnosing dermatological conditions at an stage. Data augmentation techniques were employed number samples create diverse data pattern within dataset. study applied ResNet50, InceptionV3, DenseNet121 models, leading development weighted average model. system was trained tested using International Skin Imaging Collaboration (ISIC) proposed demonstrated superior performance, achieving 98.5% accuracy, 97.50% Kappa, 97.67% MCC (Matthews Correlation Coefficient), 98.50% F1 score. outperformed existing state-of-the-art models classification provides valuable support dermatologists specialists detection. Compared previous research, offers high accuracy lower computational complexity, addressing challenge skin-related
Language: Английский
Citations
0
International Journal of Science and Research Archive, Journal Year: 2024, Volume and Issue: 12(2), P. 1399 - 1410
Published: July 30, 2024
Smart healthcare is in the process of quick evolution from traditional focused approach towards specialist and hospital to a patient-centric model. The following technological advancements have boosted this revolution vertical. Presently, 4G as well other communication standards like WLAN are applied offer smart services solutions. considers apply for advancement further future. It reason that industry expands, several applications anticipated generate huge volume data various forms sizes. Thus, enormous varying requires special end-to-end delay, bandwidth, latency factors. it becomes highly challenging current technologies effectively support complex sensitive health care these 5G networks being planned implemented address multifaceted requirements IoT. assisted consist IoT devices which need better network performance extended cellular connections. There issues with existing connectivity solutions namely how many can be connected, achieving global standardization, optimizing low power budgets, fit into given area secure communication. This paper aims provide an elaborate review by technology.
Language: Английский
Citations
3