Skin Cancer Disease Detection Using Transfer Learning Technique

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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Bibliographic Details
Published in:Applied sciences Vol. 12; no. 11; p. 5714
Main Authors: Rashid, Javed, Ishfaq, Maryam, Ali, Ghulam, Saeed, Muhammad R., Hussain, Mubasher, Alkhalifah, Tamim, Alturise, Fahad, Samand, Noor
Format: Journal Article
Language:English
Published: Basel MDPI AG 01-06-2022
Subjects:
Accuracy
Artificial intelligence
Biopsy
Classification
Computer applications
Datasets
Deep learning
Dermatology
Disease detection
ISIC-2020 dataset
Lymphatic system
Machine learning
Malignancy
malignant melanoma
Melanoma
Methods
MobileNetV2
Neural networks
Skin cancer
Skin diseases
Skin lesions
Support systems
Support vector machines
Survival
Transfer learning
Tumors
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Summary: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.
ISSN:2076-3417
2076-3417
DOI:10.3390/app12115714