The Application of Deep Learning for Skin Disease Classification Using the EfficientNet-B1 Model
The skin, being the largest organ in the human body, plays a vital role in protecting against various external threats. However, cases of skin diseases are steadily rising across countries, making it a significant global health concern. Diagnosis often faces challenges due to symptom variations and...
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| Format: | Article |
| Language: | English |
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Politeknik Negeri Batam
2025-03-01
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| Series: | Journal of Applied Informatics and Computing |
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| Online Access: | https://jurnal.polibatam.ac.id/index.php/JAIC/article/view/9100 |
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| author | Ayub Michaelangelo Manurung Ilham Santoso Egia Rosi Subhiyakto |
| author_facet | Ayub Michaelangelo Manurung Ilham Santoso Egia Rosi Subhiyakto |
| author_sort | Ayub Michaelangelo Manurung |
| collection | DOAJ |
| description | The skin, being the largest organ in the human body, plays a vital role in protecting against various external threats. However, cases of skin diseases are steadily rising across countries, making it a significant global health concern. Diagnosis often faces challenges due to symptom variations and low public awareness, highlighting the need for automated technology in skin disease detection. This study developed an automated classification system for skin diseases using EfficientNet-B1, capable of categorizing five skin conditions: Acne and Rosacea, Eczema, Melanoma Skin Cancer Nevi and Moles, Normal, Vitiligo, Psoriasis pictures Lichen Planus and related diseases, Seborrheic Keratoses and other Benign Tumors, Tinea Ringworm Candidiasis and other Fungal Infections. The system utilized 1.571 plus 1641 JPG digital images resized to 224 x 224 pixels, with 80% of the data allocated for training and 20% for testing. The trained model achieved a high accuracy of 99%, demonstrating the system's potential to support faster and more accurate diagnostic processes. |
| format | Article |
| id | doaj-art-eebbd5b774db4906b3f15eba791e72e5 |
| institution | DOAJ |
| issn | 2548-6861 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Politeknik Negeri Batam |
| record_format | Article |
| series | Journal of Applied Informatics and Computing |
| spelling | doaj-art-eebbd5b774db4906b3f15eba791e72e52025-08-20T03:19:13ZengPoliteknik Negeri BatamJournal of Applied Informatics and Computing2548-68612025-03-019231031710.30871/jaic.v9i2.91006658The Application of Deep Learning for Skin Disease Classification Using the EfficientNet-B1 ModelAyub Michaelangelo Manurung0Ilham Santoso1Egia Rosi Subhiyakto2Teknik Informatika, Fakultas Ilmu Komputer, Universitas Dian Nuswantoro, Semarang 50131, IndonesiaTeknik Informatika, Fakultas Ilmu Komputer, Universitas Dian Nuswantoro, Semarang 50131, IndonesiaTeknik Informatika, Fakultas Ilmu Komputer, Universitas Dian Nuswantoro, Semarang 50131, IndonesiaThe skin, being the largest organ in the human body, plays a vital role in protecting against various external threats. However, cases of skin diseases are steadily rising across countries, making it a significant global health concern. Diagnosis often faces challenges due to symptom variations and low public awareness, highlighting the need for automated technology in skin disease detection. This study developed an automated classification system for skin diseases using EfficientNet-B1, capable of categorizing five skin conditions: Acne and Rosacea, Eczema, Melanoma Skin Cancer Nevi and Moles, Normal, Vitiligo, Psoriasis pictures Lichen Planus and related diseases, Seborrheic Keratoses and other Benign Tumors, Tinea Ringworm Candidiasis and other Fungal Infections. The system utilized 1.571 plus 1641 JPG digital images resized to 224 x 224 pixels, with 80% of the data allocated for training and 20% for testing. The trained model achieved a high accuracy of 99%, demonstrating the system's potential to support faster and more accurate diagnostic processes.https://jurnal.polibatam.ac.id/index.php/JAIC/article/view/9100classificationdermatologyefficientnet-b1resnet50skin desease |
| spellingShingle | Ayub Michaelangelo Manurung Ilham Santoso Egia Rosi Subhiyakto The Application of Deep Learning for Skin Disease Classification Using the EfficientNet-B1 Model Journal of Applied Informatics and Computing classification dermatology efficientnet-b1 resnet50 skin desease |
| title | The Application of Deep Learning for Skin Disease Classification Using the EfficientNet-B1 Model |
| title_full | The Application of Deep Learning for Skin Disease Classification Using the EfficientNet-B1 Model |
| title_fullStr | The Application of Deep Learning for Skin Disease Classification Using the EfficientNet-B1 Model |
| title_full_unstemmed | The Application of Deep Learning for Skin Disease Classification Using the EfficientNet-B1 Model |
| title_short | The Application of Deep Learning for Skin Disease Classification Using the EfficientNet-B1 Model |
| title_sort | application of deep learning for skin disease classification using the efficientnet b1 model |
| topic | classification dermatology efficientnet-b1 resnet50 skin desease |
| url | https://jurnal.polibatam.ac.id/index.php/JAIC/article/view/9100 |
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