AI in Precision Oncology, Год журнала: 2024, Номер 1(6), С. 286 - 305
Опубликована: Дек. 1, 2024
Язык: Английский
AI in Precision Oncology, Год журнала: 2024, Номер 1(6), С. 286 - 305
Опубликована: Дек. 1, 2024
Язык: Английский
Cancers, Год журнала: 2024, Номер 16(10), С. 1870 - 1870
Опубликована: Май 14, 2024
Artificial intelligence (AI) is currently becoming a leading field in data processing [...]
Язык: Английский
Процитировано
19Scientific Reports, Год журнала: 2025, Номер 15(1)
Опубликована: Янв. 8, 2025
Skin cancer is common and deadly, hence a correct diagnosis at an early age essential. Effective therapy depends on precise classification of the several skin forms, each with special traits. Because dermoscopy other sophisticated imaging methods produce detailed lesion images, detection has been enhanced. It's still difficult to analyze images differentiate benign from malignant tumors, though. Better predictive modeling are needed since diagnostic procedures used now frequently inaccurate inconsistent results. In dermatology, Machine learning (ML) models becoming essential for automatic lesions image data. With ensemble model, which mix ML approaches take use their advantages lessen disadvantages, this work seeks improve predictions. We introduce new method, Max Voting optimization classification. On HAM10000 ISIC 2018 datasets, we trained assessed three distinct models: Random Forest (RF), Multi-layer Perceptron Neural Network (MLPN), Support Vector (SVM). Overall performance was increased by combined predictions made technique. Moreover, feature vectors that were optimally produced data Genetic Algorithm (GA) given models. demonstrate method greatly improves performance, reaching accuracy 94.70% producing best results F1-measure, recall, precision. The most dependable robust approach turned out be Voting, combines benefits numerous pre-trained provide efficient classifying lesions.
Язык: Английский
Процитировано
2Cancers, Год журнала: 2025, Номер 17(1), С. 137 - 137
Опубликована: Янв. 3, 2025
Background: Skin cancer is the most common worldwide, with melanoma being deadliest type, though it accounts for less than 5% of cases. Traditional skin detection methods are effective but often costly and time-consuming. Recent advances in artificial intelligence have improved diagnosis by helping dermatologists identify suspicious lesions. Methods: The study used datasets from two ethnic groups, sourced ISIC platform CSMU Hospital, to develop an AI diagnostic model. Eight pre-trained models, including convolutional neural networks vision transformers, were fine-tuned. three best-performing models combined into ensemble model, which underwent multiple random experiments ensure stability. To improve accuracy reduce false negatives, a two-stage classification strategy was employed: three-class model initial classification, followed binary secondary prediction benign Results: In dataset, negative rate malignant lesions significantly reduced, number cases misclassified as dropped 124 45. CSMUH negatives completely eliminated, reducing zero, resulting notable improvement precision reduction rate. Conclusions: Through proposed method, demonstrated clear success both datasets. First, can assist doctors distinguishing between patients who require urgent treatment, non-melanoma be treated later, that do not intervention. Subsequently, effectively reduces These findings highlight potential technology diagnosis, particularly resource-limited medical settings, where could become valuable clinical tool accuracy, mortality, healthcare costs.
Язык: Английский
Процитировано
1IET Image Processing, Год журнала: 2024, Номер unknown
Опубликована: Сен. 2, 2024
Abstract Melanoma, a highly prevalent and lethal form of skin cancer, has significant impact globally. The chances recovery for melanoma patients substantially improve with early detection. Currently, deep learning (DL) methods are gaining popularity in assisting the identification melanoma. Despite their high performance, relying solely on an image classifier undermines credibility application makes it difficult to understand rationale behind model's predictions highlighting need Explainable AI (XAI). This study provides survey cancer using DL techniques utilized studies from 2017 2024. Compared existing studies, authors address latest related covering several public datasets focusing segmentation, classification based convolutional neural networks vision transformers, explainability. analysis comparisons will be beneficial researchers developers this area, identify suitable used automated classification. Thereby, findings can implement support applications advancing diagnosis process.
Язык: Английский
Процитировано
6Computers, Год журнала: 2024, Номер 13(7), С. 157 - 157
Опубликована: Июнь 21, 2024
There are many different kinds of skin cancer, and an early precise diagnosis is crucial because cancer both frequent deadly. The key to effective treatment accurately classifying the various cancers, which have unique traits. Dermoscopy other advanced imaging techniques enhanced detection by providing detailed images lesions. However, interpreting these distinguish between benign malignant tumors remains a difficult task. Improved predictive modeling necessary due occurrence erroneous inconsistent outcomes in present diagnostic processes. Machine learning (ML) models become essential field dermatology for automated identification categorization lesions using image data. aim this work develop improved predictions ensemble models, combine numerous machine approaches maximize their combined strengths reduce individual shortcomings. This paper proposes fresh special approach model optimization classification: Max Voting method. We trained assessed five ISIC 2018 HAM10000 datasets: AdaBoost, CatBoost, Random Forest, Gradient Boosting, Extra Trees. Their enhance overall performance with Moreover, were fed feature vectors that optimally generated from data genetic algorithm (GA). show that, accuracy 95.80%, significantly improves when compared individually. Obtaining best results F1-measure, recall, precision, method turned out be most dependable robust. novel aspect more robustly reliably classified technique. Several pre-trained models’ benefits approach.
Язык: Английский
Процитировано
4BioMedInformatics, Год журнала: 2024, Номер 4(4), С. 2251 - 2270
Опубликована: Ноя. 14, 2024
Skin cancer is a serious health condition, as it can locally evolve into disfiguring states or metastasize to different tissues. Early detection of this disease critical because increases the effectiveness treatment, which contributes improved patient prognosis and reduced healthcare costs. Visual assessment histopathological examination are gold standards for diagnosing these types lesions. Nevertheless, processes strongly dependent on dermatologists’ experience, with excision advised only when suspected by physician. Multiple approaches have surfed over last few years, particularly those based deep learning (DL) strategies, goal assisting medical professionals in diagnosis process ultimately diminishing diagnostic uncertainty. This systematic review focused analysis relevant studies DL applications skin diagnosis. The qualitative included 164 records topic. AlexNet, ResNet-50, VGG-16, GoogLeNet architectures considered top choices obtaining best classification results, multiclassification current trend. Public databases key elements area should be maintained facilitate scientific research.
Язык: Английский
Процитировано
3Research Square (Research Square), Год журнала: 2025, Номер unknown
Опубликована: Янв. 20, 2025
Язык: Английский
Процитировано
0Precision Medical Sciences, Год журнала: 2025, Номер unknown
Опубликована: Фев. 17, 2025
Abstract The global primary health concern of skin cancer emphasizes the need for quick and accurate diagnosis to improve patient outcomes. Although, it might be challenging evaluate possible risk a spot merely by looking at feeling it. This review article offers thorough overview current breakthroughs in machine learning (ML) computer‐aided diagnostics (CAD) aim analysis classification lesions over past 6 years. paper carefully reviews whole diagnostic process: data preparation, lesion segmentation, feature extraction, selection, final classification. Analyzed are many publicly accessible datasets creative ideas including deep (DL) ML integrated with computer vision, together their impact on increasing accuracy. Given variety complexity lesions, even enormous progress, there still major obstacles. rigorously assesses methods, notes areas great challenge, provides recommendations direct next research targeted improving early detection strategies CAD systems.
Язык: Английский
Процитировано
0Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
0Hydrogen, Год журнала: 2024, Номер 5(4), С. 819 - 850
Опубликована: Ноя. 10, 2024
This study addresses the growing need for effective energy management solutions in university settings, with particular emphasis on solar–hydrogen systems. The study’s purpose is to explore integration of deep learning models, specifically MobileNetV2 and InceptionV3, enhancing fault detection capabilities AIoT-based environments, while also customizing ISO 50001:2018 standards align unique needs academic institutions. Our research employs comparative analysis two models terms their performance detecting solar panel defects assessing accuracy, loss values, computational efficiency. findings reveal that achieves 80% making it suitable resource-constrained InceptionV3 demonstrates superior accuracy 90% but requires more resources. concludes both offer distinct advantages based application scenarios, emphasizing importance balancing efficiency when selecting appropriate system management. highlights critical role continuous improvement leadership commitment successful implementation universities.
Язык: Английский
Процитировано
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