An Integrated Deep Learning Model for Skin Cancer Detection Using Hybrid Feature Fusion Technique
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866912291971137536 |
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| author | Akter, Maksuda Khatun, Rabea Talukder, Md. Alamin Islam, Md. Manowarul Uddin, Md. Ashraf |
| author_facet | Akter, Maksuda Khatun, Rabea Talukder, Md. Alamin Islam, Md. Manowarul Uddin, Md. Ashraf |
| contents | Skin cancer is a serious and potentially fatal disease caused by DNA damage. Early detection significantly increases survival rates, making accurate diagnosis crucial. In this groundbreaking study, we present a hybrid framework based on Deep Learning (DL) that achieves precise classification of benign and malignant skin lesions. Our approach begins with dataset preprocessing to enhance classification accuracy, followed by training two separate pre-trained DL models, InceptionV3 and DenseNet121. By fusing the results of each model using the weighted sum rule, our system achieves exceptional accuracy rates. Specifically, we achieve a 92.27% detection accuracy rate, 92.33% sensitivity, 92.22% specificity, 90.81% precision, and 91.57% F1-score, outperforming existing models and demonstrating the robustness and trustworthiness of our hybrid approach. Our study represents a significant advance in skin cancer diagnosis and provides a promising foundation for further research in the field. With the potential to save countless lives through earlier detection, our hybrid deep-learning approach is a game-changer in the fight against skin cancer. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_14489 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | An Integrated Deep Learning Model for Skin Cancer Detection Using Hybrid Feature Fusion Technique Akter, Maksuda Khatun, Rabea Talukder, Md. Alamin Islam, Md. Manowarul Uddin, Md. Ashraf Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Skin cancer is a serious and potentially fatal disease caused by DNA damage. Early detection significantly increases survival rates, making accurate diagnosis crucial. In this groundbreaking study, we present a hybrid framework based on Deep Learning (DL) that achieves precise classification of benign and malignant skin lesions. Our approach begins with dataset preprocessing to enhance classification accuracy, followed by training two separate pre-trained DL models, InceptionV3 and DenseNet121. By fusing the results of each model using the weighted sum rule, our system achieves exceptional accuracy rates. Specifically, we achieve a 92.27% detection accuracy rate, 92.33% sensitivity, 92.22% specificity, 90.81% precision, and 91.57% F1-score, outperforming existing models and demonstrating the robustness and trustworthiness of our hybrid approach. Our study represents a significant advance in skin cancer diagnosis and provides a promising foundation for further research in the field. With the potential to save countless lives through earlier detection, our hybrid deep-learning approach is a game-changer in the fight against skin cancer. |
| title | An Integrated Deep Learning Model for Skin Cancer Detection Using Hybrid Feature Fusion Technique |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2410.14489 |