Segmentation and classification of skin cancer diseases based on deep learning: challenges and future directions
Deep Learning (DL) techniques have significantly improved the diagnostic accuracy in healthcare, particularly for detecting and classifying skin cancer. Such technological advancements will assist healthcare professionals in delivering more accurate, efficient, and timely diagnoses, ultimately impro...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Institute of Electrical and Electronics Engineers
2025
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| Online Access: | http://psasir.upm.edu.my/id/eprint/119013/ http://psasir.upm.edu.my/id/eprint/119013/1/119013.pdf |
| Summary: | Deep Learning (DL) techniques have significantly improved the diagnostic accuracy in healthcare, particularly for detecting and classifying skin cancer. Such technological advancements will assist healthcare professionals in delivering more accurate, efficient, and timely diagnoses, ultimately improving patient outcomes and facilitating early detection and treatment. Medical imaging technologies
such as magnetic resonance imaging (MRI) and computed tomography (CT) are critical for diagnosing dermatological conditions. However, interpreting these images can be challenging due to overlapping structures and varying image quality. This study explores the application of DL in skin cancer diagnosis, focusing on advances in image segmentation and classification. DL-based models are reviewed specifically by convolutional neural networks (CNNs), and evaluations on their effectiveness for skin lesion detection are provided. This study also examines the critical challenges of deploying DL models in clinical practice,
covering issues including dataset diversity, model interpretability, and real-world implementation feasibility.
It further explores the selection of network architectures and data preprocessing techniques, emphasizing their influence on model performance. In summary, this study identifies research gaps and suggests future directions for enhancing DL models for dermatological applications. |
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