Skin Cancer Cell Detection using Image Processing
Early diagnosis and precise detection of skin cancer represent a global health priority since this disease remains highly dangerous while being among the most frequent ones. This research investigates the effectiveness of deep learning techniques, specifically Convolutional Neural Networks (CNN) and...
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| Format: | Article |
| Language: | English |
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levent
2025-06-01
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| Series: | International Journal of Pioneering Technology and Engineering |
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| Online Access: | https://ijpte.com/index.php/ijpte/article/view/122 |
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| _version_ | 1839656206213316608 |
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| author | Taskin Sabit Faiza Tasnim Sadia Afrin Sara Sharia Tasnim Adrita Maisha Tarannum |
| author_facet | Taskin Sabit Faiza Tasnim Sadia Afrin Sara Sharia Tasnim Adrita Maisha Tarannum |
| author_sort | Taskin Sabit |
| collection | DOAJ |
| description | Early diagnosis and precise detection of skin cancer represent a global health priority since this disease remains highly dangerous while being among the most frequent ones. This research investigates the effectiveness of deep learning techniques, specifically Convolutional Neural Networks (CNN) and the VGG16 architecture, for skin cancer detection and classification. The study works with images from the International Skin Imaging Collaboration (ISIC) while employing resizing and augmentation preprocessing to boost its model performance. We evaluate the proposed model using precision, recall, and F1-score metrics to ensure accurate classification. The proposed CNN model achieved 87% validation accuracy, outperforming the VGG16 model, which attained 65% accuracy. Experimental results highlight the potential of AI-driven models in improving diagnostic accuracy, demonstrating their significance in medical image analysis and early skin cancer detection. |
| format | Article |
| id | doaj-art-aff964ff9ef34d25aec6e2fca75c2ff9 |
| institution | Matheson Library |
| issn | 2822-454X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | levent |
| record_format | Article |
| series | International Journal of Pioneering Technology and Engineering |
| spelling | doaj-art-aff964ff9ef34d25aec6e2fca75c2ff92025-06-24T19:36:56ZengleventInternational Journal of Pioneering Technology and Engineering2822-454X2025-06-01401263610.56158/jpte.2025.122.4.01122Skin Cancer Cell Detection using Image ProcessingTaskin Sabit0https://orcid.org/0009-0004-4950-2199Faiza Tasnim1https://orcid.org/0009-0007-5395-8756Sadia Afrin Sara2https://orcid.org/0009-0005-8183-0157Sharia Tasnim Adrita3https://orcid.org/0009-0002-2619-0837Maisha Tarannum4https://orcid.org/0009-0006-6878-5918Department of Pharmacology and Toxicology, Master of Science, Wright State University, Dayton, OH, USA.Computer Science and Engineering, Bachelor of Science, American International University-Bangladesh, Dhaka, Bangladesh.Computer Science and Engineering, Bachelor of Science, American International University-Bangladesh, Dhaka, Bangladesh.Computer Science and Engineering, Bachelor of Science, American International University-Bangladesh, Dhaka, Bangladesh.Biomedical Engineering, Bachelor of Science, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh.Early diagnosis and precise detection of skin cancer represent a global health priority since this disease remains highly dangerous while being among the most frequent ones. This research investigates the effectiveness of deep learning techniques, specifically Convolutional Neural Networks (CNN) and the VGG16 architecture, for skin cancer detection and classification. The study works with images from the International Skin Imaging Collaboration (ISIC) while employing resizing and augmentation preprocessing to boost its model performance. We evaluate the proposed model using precision, recall, and F1-score metrics to ensure accurate classification. The proposed CNN model achieved 87% validation accuracy, outperforming the VGG16 model, which attained 65% accuracy. Experimental results highlight the potential of AI-driven models in improving diagnostic accuracy, demonstrating their significance in medical image analysis and early skin cancer detection.https://ijpte.com/index.php/ijpte/article/view/122deep learningconvolutional neural networks (cnn)vgg16classification |
| spellingShingle | Taskin Sabit Faiza Tasnim Sadia Afrin Sara Sharia Tasnim Adrita Maisha Tarannum Skin Cancer Cell Detection using Image Processing International Journal of Pioneering Technology and Engineering deep learning convolutional neural networks (cnn) vgg16 classification |
| title | Skin Cancer Cell Detection using Image Processing |
| title_full | Skin Cancer Cell Detection using Image Processing |
| title_fullStr | Skin Cancer Cell Detection using Image Processing |
| title_full_unstemmed | Skin Cancer Cell Detection using Image Processing |
| title_short | Skin Cancer Cell Detection using Image Processing |
| title_sort | skin cancer cell detection using image processing |
| topic | deep learning convolutional neural networks (cnn) vgg16 classification |
| url | https://ijpte.com/index.php/ijpte/article/view/122 |
| work_keys_str_mv | AT taskinsabit skincancercelldetectionusingimageprocessing AT faizatasnim skincancercelldetectionusingimageprocessing AT sadiaafrinsara skincancercelldetectionusingimageprocessing AT shariatasnimadrita skincancercelldetectionusingimageprocessing AT maishatarannum skincancercelldetectionusingimageprocessing |