Skin cancer detection using dermoscopic images with convolutional neural network

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

Published: March 1, 2025

Skin malignant melanoma is a high-risk tumor with low incidence but high mortality rates. Early detection and treatment are crucial for cure. Machine learning studies have focused on classifying tumors, these methods cumbersome fail to extract deeper features. This limits their ability distinguish subtle variations in skin lesions accurately, hindering effective early diagnosis. The study introduces deep learning-based network specifically designed lesion enhance data the dataset. It leverages novel FCDS-CNN architecture address class-imbalanced problems improve quality. Specifically, incorporates augmentation class weighting techniques mitigate impact of imbalanced classes. also presents practical, large-scale solution that allows seamless, real-world incorporation support dermatologists screening processes. proposed robust model performance across all lesions. dataset includes 10015 images seven classes available Kaggle. To overcome dominance one over other, like used. showed improved accuracy an average 96%, outperforming pre-trained models such as ResNet, EfficientNet, Inception, MobileNet precision, recall, F1-score, area under curve parameters. These more general image classification struggle nuanced features imbalances inherent medical datasets. demonstrated practical effectiveness by compared based distinct work testament importance specificity analysis regarding cancer detection.

Language: Английский

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An intelligent framework for skin cancer detection and classification using fusion of Squeeze-Excitation-DenseNet with Metaheuristic-driven ensemble deep learning models

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

Published: March 3, 2025

Skin cancer is the most dominant and critical method of cancer, which arises all over world. Its damaging effects can range from disfigurement to major medical expenditures even death if not analyzed preserved timely. Conventional models skin recognition require a complete physical examination by specialist, time-wasting in few cases. Computer-aided medicinal analytical methods have gained massive popularity due their efficiency effectiveness. This model assist dermatologists initial significant for early diagnosis. An automatic classification utilizing deep learning (DL) help doctors perceive kind lesion improve patient's health. The one hot topics research field, along with development DL structure. manuscript designs develops Detection Cancer Using an Ensemble Deep Learning Model Gray Wolf Optimization (DSC-EDLMGWO) method. proposed DSC-EDLMGWO relies on biomedical imaging. presented initially involves image preprocessing stage at two levels: contract enhancement using CLAHE noise removal wiener filter (WF) model. Furthermore, utilizes SE-DenseNet method, fusion squeeze-and-excitation (SE) module DenseNet extract features. For process, ensemble models, namely long short-term memory (LSTM) technique, extreme machine (ELM) model, stacked sparse denoising autoencoder (SSDA) employed. Finally, gray wolf optimization (GWO) optimally adjusts models' hyperparameter values, resulting more excellent performance. effectiveness approach evaluated benchmark database, outcomes measured across various performance metrics. experimental validation portrayed superior accuracy value 98.38% 98.17% under HAM10000 ISIC datasets other techniques.

Language: Английский

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AI Dermatology: Reviewing the Frontiers of Skin Cancer Detection Technologies

Published: March 1, 2025

Language: Английский

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A comprehensive deep learning approach for skin cancer diagnosis: integrating interpretability and advanced techniques

Multimedia Tools and Applications, Journal Year: 2025, Volume and Issue: unknown

Published: May 2, 2025

Language: Английский

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Enhancing Skin Cancer Diagnosis Through Fine‐Tuning of Pretrained Models: A Two‐Phase Transfer Learning Approach

International Journal of Breast Cancer, Journal Year: 2025, Volume and Issue: 2025(1)

Published: Jan. 1, 2025

Skin cancer is among the most prevalent types of worldwide, and early detection crucial for improving treatment outcomes patient survival rates. Traditional diagnostic methods, often reliant on visual examination manual evaluation, can be subjective time-consuming, leading to variability in accuracy. Recent developments machine learning, particularly using pretrained models fine-tuning techniques, offer promising advancements automating skin classification. This paper explores application a two-phase model HAM10000 dataset, which comprises wide range lesion images. The first phase employs transfer learning with frozen layers, followed by all layers second adapt more specifically dataset. I evaluate nine models, including VGG16, VGG19, InceptionV3, Xception (extreme inception), DenseNet121, assessing their performance based accuracy, precision, recall, F1 score metrics. VGG16 model, after fine-tuning, achieved highest test set accuracy 99.3%, highlighting its potential highly accurate study provides important insights clinicians researchers, demonstrating efficacy advanced enhancing supporting clinical decision-making dermatology.

Language: Английский

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Cilt Kanseri Tanısı için Farklı Evrişimsel Sinir Ağı Modellerinin Karşılaştırılması

Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi, Journal Year: 2025, Volume and Issue: 15(1), P. 25 - 38

Published: Feb. 19, 2025

Son yıllarda, dünya genelinde cilt kanseri görülme oranında önemli bir artış gözlemlenmektedir. Cilt kanserinin zamanında ve doğru şekilde teşhis edilmesi, tedavi başarı oranlarını artırmakta aynı zamanda hastaların yaşam kalitesinin iyileşmesine büyük katkı sağlamaktadır. Geleneksel tanı yöntemleri genellikle görsel değerlendirmelere dayanmakta öznel yaklaşım içermektedir. Bununla birlikte, derin öğrenme algoritmaları, teşhislerinin doğruluğunu verimliliğini artırmak için etkili çözümler sunmaktadır. Bu çalışmada, EfficientNet, VGG, Inception, DenseNet DarkNet gibi gelişmiş Evrişimsel Sinir Ağı (CNN) modellerinin sınıflandırmasındaki performansları incelenmiştir. Toplamda yirmi CNN modeli, ISIC 2017 veri seti üzerinde, artırma transfer teknikleri kullanılarak eğitilmiş detaylı değerlendirilmiştir. Deneysel sonuçlar, EfficientNet-b0 modelinin %84.00 doğruluk, %83.63 kesinlik, %74.96 duyarlılık %78.59 F1-skoru ile en yüksek performansı sergilediğini göstermiştir. kapsamlı analiz, tabanlı modellerin teşhisindeki etkinliğini göstermekte gelecekteki araştırmalar bu algoritmaların potansiyelini ortaya koymaktadır.

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A Multimodal Deep Ensemble Framework for Skin Lesion Classification

Lecture notes in computer science, Journal Year: 2025, Volume and Issue: unknown, P. 100 - 111

Published: Jan. 1, 2025

Language: Английский

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Skin Cancer Image Classification Using Artificial Intelligence Strategies: A Systematic Review

Journal of Imaging, Journal Year: 2024, Volume and Issue: 10(11), P. 265 - 265

Published: Oct. 22, 2024

The increasing incidence of and resulting deaths associated with malignant skin tumors are a public health problem that can be minimized if detection strategies improved. Currently, diagnosis is heavily based on physicians' judgment experience, which occasionally lead to the worsening lesion or needless biopsies. Several non-invasive imaging modalities, e.g., confocal scanning laser microscopy multiphoton microscopy, have been explored for cancer assessment, aligned different artificial intelligence (AI) assist in diagnostic task, several image features, thus making process more reliable faster. This systematic review concerns implementation AI methods tumor classification following PRISMA guidelines. In total, 206 records were retrieved qualitatively analyzed. Diagnostic potential was found techniques, particularly dermoscopy images, yielding results close perfection. Learning approaches support vector machines neural networks seem preferred, recent focus convolutional networks. Still, detailed descriptions training/testing conditions lacking some reports, hampering reproduction. use an expanding field, future work aiming construct optimal learning strategies. Ultimately, early could optimized, improving patient outcomes, even areas where healthcare scarce.

Language: Английский

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Skin Cancer Prediction by Incorporating Bio-inspired Optimization in Deep Neural Network

SN Computer Science, Journal Year: 2024, Volume and Issue: 5(8)

Published: Dec. 2, 2024

Language: Английский

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