Skin Cancer Classification Using a Hybrid Pre-trained CNN with Random Forest Classifier
Skin cancer, a leading cause of global mortality with 10 million deaths annually, is projected to increase rapidly if not diagnosed early. Traditional diagnosis relies on visual evaluation and histopathology, which are subjective and time-consuming. Recent advances in Convolutional Neural Networks (...
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| Format: | Article |
| Language: | English |
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Ikatan Ahli Informatika Indonesia
2024-08-01
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| Series: | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
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| Online Access: | https://jurnal.iaii.or.id/index.php/RESTI/article/view/5857 |
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| _version_ | 1841544038155550720 |
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| author | Okky Darmawan Kostidjan Yudhi Purwanto Anny Yuniarti |
| author_facet | Okky Darmawan Kostidjan Yudhi Purwanto Anny Yuniarti |
| author_sort | Okky Darmawan Kostidjan |
| collection | DOAJ |
| description | Skin cancer, a leading cause of global mortality with 10 million deaths annually, is projected to increase rapidly if not diagnosed early. Traditional diagnosis relies on visual evaluation and histopathology, which are subjective and time-consuming. Recent advances in Convolutional Neural Networks (CNN) enable automated, accurate image analysis for early identification. This study explores pre-trained CNN models, including DenseNet-201, InceptionV3, MobileNet, ResNet50, and VGG16, by modifying them to better identify skin lesions as malignant or benign. The proposed models outperformed the state-of-the-art CNN models evaluated on publicity with traditional test data. The proposed models achieved 94.20% accuracy, which is higher than that of traditional CNN models. |
| format | Article |
| id | doaj-art-42182929de144d04886af500eb221c90 |
| institution | Kabale University |
| issn | 2580-0760 |
| language | English |
| publishDate | 2024-08-01 |
| publisher | Ikatan Ahli Informatika Indonesia |
| record_format | Article |
| series | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
| spelling | doaj-art-42182929de144d04886af500eb221c902025-01-13T03:33:02ZengIkatan Ahli Informatika IndonesiaJurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)2580-07602024-08-018450651510.29207/resti.v8i4.58575857Skin Cancer Classification Using a Hybrid Pre-trained CNN with Random Forest ClassifierOkky Darmawan Kostidjan0Yudhi Purwanto1Anny Yuniarti2Institut Teknologi Sepuluh NopemberInstitut Teknologi Sepuluh NopemberInstitut Teknologi Sepuluh NopemberSkin cancer, a leading cause of global mortality with 10 million deaths annually, is projected to increase rapidly if not diagnosed early. Traditional diagnosis relies on visual evaluation and histopathology, which are subjective and time-consuming. Recent advances in Convolutional Neural Networks (CNN) enable automated, accurate image analysis for early identification. This study explores pre-trained CNN models, including DenseNet-201, InceptionV3, MobileNet, ResNet50, and VGG16, by modifying them to better identify skin lesions as malignant or benign. The proposed models outperformed the state-of-the-art CNN models evaluated on publicity with traditional test data. The proposed models achieved 94.20% accuracy, which is higher than that of traditional CNN models.https://jurnal.iaii.or.id/index.php/RESTI/article/view/5857skin cancerclassificationcnn pre-trainedhybrid model |
| spellingShingle | Okky Darmawan Kostidjan Yudhi Purwanto Anny Yuniarti Skin Cancer Classification Using a Hybrid Pre-trained CNN with Random Forest Classifier Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) skin cancer classification cnn pre-trained hybrid model |
| title | Skin Cancer Classification Using a Hybrid Pre-trained CNN with Random Forest Classifier |
| title_full | Skin Cancer Classification Using a Hybrid Pre-trained CNN with Random Forest Classifier |
| title_fullStr | Skin Cancer Classification Using a Hybrid Pre-trained CNN with Random Forest Classifier |
| title_full_unstemmed | Skin Cancer Classification Using a Hybrid Pre-trained CNN with Random Forest Classifier |
| title_short | Skin Cancer Classification Using a Hybrid Pre-trained CNN with Random Forest Classifier |
| title_sort | skin cancer classification using a hybrid pre trained cnn with random forest classifier |
| topic | skin cancer classification cnn pre-trained hybrid model |
| url | https://jurnal.iaii.or.id/index.php/RESTI/article/view/5857 |
| work_keys_str_mv | AT okkydarmawankostidjan skincancerclassificationusingahybridpretrainedcnnwithrandomforestclassifier AT yudhipurwanto skincancerclassificationusingahybridpretrainedcnnwithrandomforestclassifier AT annyyuniarti skincancerclassificationusingahybridpretrainedcnnwithrandomforestclassifier |