Skin Cancer Disease Detection Using Transfer Learning Technique
Melanoma is a fatal type of skin cancer; the fury spread results in a high fatality rate when the malignancy is not treated at an initial stage. The patients’ lives can be saved by accurately detecting skin cancer at an initial stage. A quick and precise diagnosis might help increase the patient’s s...
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| Published in: | Applied sciences Vol. 12; no. 11; p. 5714 |
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| Main Authors: | , , , , , , , |
| Format: | Journal Article |
| Language: | English |
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Basel
MDPI AG
01.06.2022
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| ISSN: | 2076-3417, 2076-3417 |
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| Abstract | Melanoma is a fatal type of skin cancer; the fury spread results in a high fatality rate when the malignancy is not treated at an initial stage. The patients’ lives can be saved by accurately detecting skin cancer at an initial stage. A quick and precise diagnosis might help increase the patient’s survival rate. It necessitates the development of a computer-assisted diagnostic support system. This research proposes a novel deep transfer learning model for melanoma classification using MobileNetV2. The MobileNetV2 is a deep convolutional neural network that classifies the sample skin lesions as malignant or benign. The performance of the proposed deep learning model is evaluated using the ISIC 2020 dataset. The dataset contains less than 2% malignant samples, raising the class imbalance. Various data augmentation techniques were applied to tackle the class imbalance issue and add diversity to the dataset. The experimental results demonstrate that the proposed deep learning technique outperforms state-of-the-art deep learning techniques in terms of accuracy and computational cost. |
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| AbstractList | Melanoma is a fatal type of skin cancer; the fury spread results in a high fatality rate when the malignancy is not treated at an initial stage. The patients’ lives can be saved by accurately detecting skin cancer at an initial stage. A quick and precise diagnosis might help increase the patient’s survival rate. It necessitates the development of a computer-assisted diagnostic support system. This research proposes a novel deep transfer learning model for melanoma classification using MobileNetV2. The MobileNetV2 is a deep convolutional neural network that classifies the sample skin lesions as malignant or benign. The performance of the proposed deep learning model is evaluated using the ISIC 2020 dataset. The dataset contains less than 2% malignant samples, raising the class imbalance. Various data augmentation techniques were applied to tackle the class imbalance issue and add diversity to the dataset. The experimental results demonstrate that the proposed deep learning technique outperforms state-of-the-art deep learning techniques in terms of accuracy and computational cost. |
| Author | Alturise, Fahad Samand, Noor Ali, Ghulam Hussain, Mubasher Rashid, Javed Saeed, Muhammad R. Ishfaq, Maryam Alkhalifah, Tamim |
| Author_xml | – sequence: 1 givenname: Javed orcidid: 0000-0003-3416-9720 surname: Rashid fullname: Rashid, Javed – sequence: 2 givenname: Maryam surname: Ishfaq fullname: Ishfaq, Maryam – sequence: 3 givenname: Ghulam orcidid: 0000-0002-0726-2738 surname: Ali fullname: Ali, Ghulam – sequence: 4 givenname: Muhammad R. surname: Saeed fullname: Saeed, Muhammad R. – sequence: 5 givenname: Mubasher surname: Hussain fullname: Hussain, Mubasher – sequence: 6 givenname: Tamim orcidid: 0000-0001-8407-2068 surname: Alkhalifah fullname: Alkhalifah, Tamim – sequence: 7 givenname: Fahad orcidid: 0000-0001-9176-7984 surname: Alturise fullname: Alturise, Fahad – sequence: 8 givenname: Noor surname: Samand fullname: Samand, Noor |
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| Snippet | Melanoma is a fatal type of skin cancer; the fury spread results in a high fatality rate when the malignancy is not treated at an initial stage. The patients’... |
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| SubjectTerms | Accuracy Artificial intelligence Biopsy Classification Datasets Deep learning Dermatology ISIC-2020 dataset Lymphatic system Machine learning malignant melanoma Melanoma Methods MobileNetV2 Neural networks Skin cancer Skin diseases Support vector machines Tumors |
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| Title | Skin Cancer Disease Detection Using Transfer Learning Technique |
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| Volume | 12 |
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