Innovative approaches for skin disease identification in machine learning: A comprehensive study
Skin diseases encompass a vast array of conditions, ranging from common dermatological concerns to rare and complex disorders, collectively posing a significant burden on global healthcare systems. For these illnesses to be managed and treated effectively, prompt and correct diagnosis is essential,...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-06-01
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| Series: | Oral Oncology Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772906024002115 |
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| Summary: | Skin diseases encompass a vast array of conditions, ranging from common dermatological concerns to rare and complex disorders, collectively posing a significant burden on global healthcare systems. For these illnesses to be managed and treated effectively, prompt and correct diagnosis is essential, yet it often presents a challenge due to the subjective nature of visual examination and the variability in clinical presentations. The field of dermatology has seen a change in recent years due to the convergence of artificial intelligence and medicine, which has produced creative methods for computer-aided diagnostics. Machine learning has become a potent tool in the search for more precise and effective diagnostic techniques because of its capacity to analyze enormous volumes of data and identify intricate patterns. This review paper explores the state-of-the-art developments in machine learning methods designed especially for skin disease identification. Investigate the effectiveness and performance of several algorithms, such as the flexible k-nearest neighbor, the sturdy support vector machine (SVM), and the complex convolutional neural networks (CNNs), advanced techniques for automated skin disease detection encompass deep learning methods such as recurrent neural networks (RNNs) for sequential data processing, generative adversarial networks (GANs) for generating synthetic data, and attention mechanisms for focusing on relevant image regions by means of a thorough examination of the most recent studies. Each algorithm is scrutinized for its strengths and limitations, providing valuable insights into their applicability in dermatological practice. This study intends to promote a broader knowledge of machine learning's potential to transform the diagnosis and treatment of skin disorders, eventually increasing patient outcomes and boosting the provision of healthcare services, by putting light on the field's developing developments in dermatology. |
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| ISSN: | 2772-9060 |