Diagnosis and prognosis of melanoma from dermoscopy images using machine learning and deep learning: a systematic literature review

BMC Cancer, Journal Year: 2025, Volume and Issue: 25(1)

Published: Jan. 13, 2025

Melanoma is a highly aggressive skin cancer, where early and accurate diagnosis crucial to improve patient outcomes. Dermoscopy, non-invasive imaging technique, aids in melanoma detection but can be limited by subjective interpretation. Recently, machine learning deep techniques have shown promise enhancing diagnostic precision automating the analysis of dermoscopy images. This systematic review examines recent advancements (ML) (DL) applications for prognosis using We conducted thorough search across multiple databases, ultimately reviewing 34 studies published between 2016 2024. The covers range model architectures, including DenseNet ResNet, discusses datasets, methodologies, evaluation metrics used validate performance. Our results highlight that certain such as DCNN demonstrated outstanding performance, achieving over 95% accuracy on HAM10000, ISIC other datasets from provides insights into strengths, limitations, future research directions methods prognosis. It emphasizes challenges related data diversity, interpretability, computational resource requirements. underscores potential transform through improved efficiency. Future should focus creating accessible, large interpretability increase clinical applicability. By addressing these areas, models could play central role advancing care.

Language: Английский

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Towards unbiased skin cancer classification using deep feature fusion

BMC Medical Informatics and Decision Making, Journal Year: 2025, Volume and Issue: 25(1)

Published: Jan. 31, 2025

Abstract This paper introduces SkinWiseNet (SWNet), a deep convolutional neural network designed for the detection and automatic classification of potentially malignant skin cancer conditions. SWNet optimizes feature extraction through multiple pathways, emphasizing width augmentation to enhance efficiency. The proposed model addresses potential biases associated with conditions, particularly in individuals darker tones or excessive hair, by incorporating fusion assimilate insights from diverse datasets. Extensive experiments were conducted using publicly accessible datasets evaluate SWNet’s effectiveness.This study utilized four datasets-Mnist-HAM10000, ISIC2019, ISIC2020, Melanoma Skin Cancer-comprising images categorized into benign classes. Explainable Artificial Intelligence (XAI) techniques, specifically Grad-CAM, employed interpretability model’s decisions. Comparative analysis was performed three pre-existing learning networks-EfficientNet, MobileNet, Darknet. results demonstrate superiority, achieving an accuracy 99.86% F1 score 99.95%, underscoring its efficacy gradient propagation capture across various levels. research highlights significant advancing classification, providing robust tool accurate early diagnosis. integration enhances mitigates hair tones. outcomes this contribute improved patient healthcare practices, showcasing exceptional capabilities classification.

Language: Английский

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A fuzzy rank-based deep ensemble methodology for multi-class skin cancer classification

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Feb. 20, 2025

Skin cancer is widespread and can be potentially fatal. According to the World Health Organisation (WHO), it has been identified as a leading cause of mortality. It essential detect skin early so that effective treatment provided at an initial stage. In this study, widely-used HAM10000 dataset, containing high-resolution images various lesions, employed train evaluate. Our methodology for dataset involves balancing imbalanced by augmenting followed splitting into train, test validation set, preprocessing images, training individual models Xception, InceptionResNetV2 MobileNetV2, then combining their outputs using fuzzy logic generate final prediction. We examined performance ensemble standard metrics like classification accuracy, confusion matrix, etc. achieved impressive accuracy 95.14% result demonstrates effectiveness our approach in accurately identifying lesions. To further assess efficiency model, additional tests have performed on DermaMNIST from MedMNISTv2 collection. The model performs well transcends benchmark 76.8%, achieving 78.25%. Thus efficient classification, showcasing its potential clinical applications.

Language: Английский

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A Computing Framework for Transfer Learning and Ensemble Classification of Surface Patterns

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

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Evaluation of an acne lesion detection and severity grading model for Chinese population in online and offline healthcare scenarios

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 7, 2025

Accurate acne severity grading is crucial for effective clinical treatment and timely follow-up management. Although some artificial intelligence methods have been developed to automate the process of grading, diversity image capture sources various application scenarios can affect their performance. Therefore, it's necessary design special evaluate them systematically before introducing into practice. To develop a deep learning-based algorithm that could accurately accomplish lesion detection simultaneously in different healthcare scenarios. We collected 2,157 facial images from two public three self-built datasets model development evaluation. An called AcneDGNet was constructed with feature extraction module, module module. Its performance evaluated both online offline Experimental results on largest most diverse evaluation revealed overall achieved accuracies 89.5% 89.8% For visits scenarios, accuracy detecting changing trends reached 87.8%, total counting error 1.91 ± 3.28 all lesions. Additionally, prospective demonstrated not only much more accurate than junior dermatologists but also comparable senior dermatologists. These findings indicated effectively assist patients diagnosis management acne,

Language: Английский

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Transformative Advances in AI for Precise Cancer Detection: A Comprehensive Review of Non-Invasive Techniques

Archives of Computational Methods in Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 11, 2025

Language: Английский

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Minimal sourced and lightweight federated transfer learning models for skin cancer detection

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 21, 2025

Language: Английский

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Numerical Analysis of Time-Fractional Cancer Models with Different Types of Net Killing Rate

Mathematics, Journal Year: 2025, Volume and Issue: 13(3), P. 536 - 536

Published: Feb. 6, 2025

This study introduces a novel approach to modeling cancer tumor dynamics within fractional framework, emphasizing the critical role of net killing rate in determining growth or decay. We explore generalized model where is considered across three domains: time-dependent, position-dependent, and concentration-dependent. The primary objective derive an analytical solution for time-fractional models using Residual Power Series Method (RPSM), technique not previously applied this conformable context. Traditional methods solving fractional-order differential face challenges such as perturbations, complex simplifications, discretization issues, restrictive assumptions. In contrast, RPSM overcomes these limitations by offering robust that reduces both complexity computational effort. method provides exact solutions through convergence series reliable numerical approximations when needed, making it versatile tool simulating models. Graphical representations approximate illustrate method’s effectiveness applicability. findings highlight RPSM’s potential advance treatment strategies providing more precise understanding work contributes theoretical practical advancements research lays groundwork accurate efficient dynamics, ultimately aiding development optimal strategies.

Language: Английский

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YOLOSAMIC: A Hybrid Approach to Skin Cancer Segmentation with the Segment Anything Model and YOLOv8

Diagnostics, Journal Year: 2025, Volume and Issue: 15(4), P. 479 - 479

Published: Feb. 16, 2025

Background/Objective: The rising global incidence of skin cancer emphasizes the urgent need for reliable and accurate diagnostic tools to aid early intervention. This study introduces YOLOSAMIC (YOLO SAM in Cancer Imaging), a fully automated segmentation framework that integrates YOLOv8 lesion detection, Segment Anything Model (SAM)-Box precise segmentation. objective is develop system handles complex characteristics without requiring manual Methods: A hybrid database comprising 3463 public 765 private dermoscopy images was built enhance model generalizability. employed localize lesions through bounding box while SAM-Box refined process. trained evaluated under four scenarios assess its robustness. Additionally, an ablation examined impact grayscale conversion, image blur, pruning on performance. Results: demonstrated high accuracy, achieving Dice Jaccard scores 0.9399 0.9112 0.8990 0.8445 dataset. Conclusions: proposed provides robust, solution segmentation, eliminating annotation. Integrating enhances precision, making it valuable decision-support tool dermatologists.

Language: Английский

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Next-generation approach to skin disorder prediction employing hybrid deep transfer learning

Frontiers in Big Data, Journal Year: 2025, Volume and Issue: 8

Published: Feb. 19, 2025

Skin diseases significantly impact individuals' health and mental wellbeing. However, their classification remains challenging due to complex lesion characteristics, overlapping symptoms, limited annotated datasets. Traditional convolutional neural networks (CNNs) often struggle with generalization, leading suboptimal performance. To address these challenges, this study proposes a Hybrid Deep Transfer Learning Method (HDTLM) that integrates DenseNet121 EfficientNetB0 for improved skin disease prediction. The proposed hybrid model leverages DenseNet121's dense connectivity capturing intricate patterns EfficientNetB0's computational efficiency scalability. A dataset comprising 19 conditions 19,171 images was used training validation. evaluated using multiple performance metrics, including accuracy, precision, recall, F1-score. Additionally, comparative analysis conducted against state-of-the-art models such as DenseNet121, EfficientNetB0, VGG19, MobileNetV2, AlexNet. HDTLM achieved accuracy of 98.18% validation 97.57%. It consistently outperformed baseline models, achieving precision 0.95, recall 0.96, F1-score an overall 98.18%. results demonstrate the model's superior ability generalize across diverse categories. findings underscore effectiveness in enhancing classification, particularly scenarios significant domain shifts labeled data. By integrating complementary strengths provides robust scalable solution automated dermatological diagnostics.

Language: Английский

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