Enhanced Skin Lesion Classification Using Deep Learning, Integrating with Sequential Data Analysis: A Multiclass Approach
In dermatological research, accurately identifying different types of skin lesions, such as nodules, is essential for early diagnosis and effective treatment. This study introduces a novel method for classifying skin lesions, including nodules, by combining a unified attention (UA) network with deep...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
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| Series: | Engineering Proceedings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-4591/78/1/6 |
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| Summary: | In dermatological research, accurately identifying different types of skin lesions, such as nodules, is essential for early diagnosis and effective treatment. This study introduces a novel method for classifying skin lesions, including nodules, by combining a unified attention (UA) network with deep convolutional neural networks (DCNNs) for feature extraction. The UA network processes sequential data, such as patient histories, while long short-term memory (LSTM) networks track nodule progression. Additionally, Markov random fields (MRFs) enhance pattern recognition. The integrated system classifies lesions and evaluates whether they are responding to treatment or worsening, achieving 93% accuracy in distinguishing nodules, melanoma, and basal cell carcinoma. This system outperforms existing methods in precision and sensitivity, offering advancements in dermatological diagnostics. |
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| ISSN: | 2673-4591 |