Diagnosis of the Skin Cancer by Vision Transformers
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| Názov: | Diagnosis of the Skin Cancer by Vision Transformers |
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| Autori: | Uğur Demiroğlu |
| Zdroj: | Düzce Üniversitesi Bilim ve Teknoloji Dergisi, Vol 13, Iss 1, Pp 588-598 (2025) Volume: 13, Issue: 1588-598 Düzce Üniversitesi Bilim ve Teknoloji Dergisi Duzce University Journal of Science and Technology |
| Informácie o vydavateľovi: | Duzce Universitesi Bilim ve Teknoloji Dergisi, 2025. |
| Rok vydania: | 2025 |
| Predmety: | categorized images, Technology, Science (General), skin cancer, vision transformers, cilt kanseri, Yapay Görme, Science, Derin Öğrenme, Machine Vision, Engineering (General). Civil engineering (General), Skin cancer, Categorized images, Malignant and benign classes, Classification, Vision transformers, Q1-390, Deep Learning, malignant and benign classes, classification, kategorize edilmiş görüntüler, Cilt kanseri, Kategorize edilmiş görüntüler, Kötü huylu ve iyi huylu sınıflar, Sınıflandırma, sınıflandırma, TA1-2040, kötü huylu ve iyi huylu sınıflar |
| Popis: | Skin cancer, one of the most frequent cancers, requires early identification for efficient treatment and better survival. Early diagnosis relies on precise and quick skin lesion categorization into benign and malignant categories. This work uses Vision Transformers (ViTs) to classify skin cancer photos by modeling long-range relationships and capturing complicated visual patterns. ViTs, originally created for natural language processing, have showed great potential in picture classification tasks because to their self-attention processes, outperforming CNNs. A public collection of 270 skin lesion images—240 malignant and 30 benign—was used in this study. Preprocessing included scaling and normalizing the dataset to 384x384x3 and splitting it into 80% training and 20% testing sets. Transfer learning optimised a pre-trained ViT model for this job. To improve accuracy and avoid overfitting, hyperparameters were carefully selected for network training. Parallel computing accelerated training to 30 minutes and 20 seconds. Vision Transformers classify medical images well, according to the study. The ViT model outperformed numerous other methods with 98.15% accuracy on the test set. A confusion matrix analysis showed great sensitivity in detecting malignant lesions and low misclassification of benign patients. These findings show that ViTs can capture detailed medical picture aspects, making them useful for dermatological diagnoses. This study shows that Vision Transformers can improve diagnosis accuracy and lays the groundwork for their use in other medical imaging fields. In the battle against skin cancer and other illnesses that need early diagnosis, ViTs' scalability, efficiency, and precision are important. Future research might integrate ViTs with other deep learning architectures to improve robustness and flexibility. This study adds to the data supporting sophisticated AI in medical diagnostics and lays the groundwork for automated, reliable, and efficient healthcare solutions. |
| Druh dokumentu: | Article |
| Popis súboru: | application/pdf |
| ISSN: | 2148-2446 |
| DOI: | 10.29130/dubited.1572317 |
| Prístupová URL adresa: | https://doaj.org/article/f17ef317a9cb42d38d80abc56588904f https://dergipark.org.tr/tr/pub/dubited/issue/90184/1572317 |
| Prístupové číslo: | edsair.doi.dedup.....4268b2d508886734fad1962fa6ee616c |
| Databáza: | OpenAIRE |
Nájsť tento článok vo Web of Science | Abstrakt: | Skin cancer, one of the most frequent cancers, requires early identification for efficient treatment and better survival. Early diagnosis relies on precise and quick skin lesion categorization into benign and malignant categories. This work uses Vision Transformers (ViTs) to classify skin cancer photos by modeling long-range relationships and capturing complicated visual patterns. ViTs, originally created for natural language processing, have showed great potential in picture classification tasks because to their self-attention processes, outperforming CNNs. A public collection of 270 skin lesion images—240 malignant and 30 benign—was used in this study. Preprocessing included scaling and normalizing the dataset to 384x384x3 and splitting it into 80% training and 20% testing sets. Transfer learning optimised a pre-trained ViT model for this job. To improve accuracy and avoid overfitting, hyperparameters were carefully selected for network training. Parallel computing accelerated training to 30 minutes and 20 seconds. Vision Transformers classify medical images well, according to the study. The ViT model outperformed numerous other methods with 98.15% accuracy on the test set. A confusion matrix analysis showed great sensitivity in detecting malignant lesions and low misclassification of benign patients. These findings show that ViTs can capture detailed medical picture aspects, making them useful for dermatological diagnoses. This study shows that Vision Transformers can improve diagnosis accuracy and lays the groundwork for their use in other medical imaging fields. In the battle against skin cancer and other illnesses that need early diagnosis, ViTs' scalability, efficiency, and precision are important. Future research might integrate ViTs with other deep learning architectures to improve robustness and flexibility. This study adds to the data supporting sophisticated AI in medical diagnostics and lays the groundwork for automated, reliable, and efficient healthcare solutions. |
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| ISSN: | 21482446 |
| DOI: | 10.29130/dubited.1572317 |