The deep learning for skin disease diagnosis and classification: A review of cutting-edge techniques, outcomes, and limitations at a glance

• Main Authors: Tyagi, Lavi, Singh, Dinesh, Goyal, Nitin
• Format: Conference Proceeding
• Language: English
• Subjects: Artificial intelligence; Classification; Deep learning; Dermatology; Diagnosis; Image processing; Imaging techniques; Machine learning; Medical imaging; Medical research; Skin; Skin diseases
• ISSN: 0094-243X; 1551-7616
• DOI: 10.1063/5.0234321

The use of medical imaging is to provide detectable representations of the internal anatomy of the body for research, diagnosis, and treatment. It gives us a visual depiction of the inside tissues’ functioning. It helps a lot in the identification and investigation of diseases. Every week, numerous imaging techniques are used worldwide, and as a result, a lot of data is generated. It is important in a variety of clinical applications. The field of medical imaging is expanding significantly as a result of advancements in image processing techniques. If combined, artificial intelligence (AI) has greatly advanced this field. If skin diseases are considered, it’s a major health problem worldwide. Deep learning (DL) and machine learning (ML) techniques are used in diagnostic imaging to classify diseases and identify their presence or absence. Despite being widespread, diagnosing skin diseases is quite challenging due to the complexity of traits such as skin tone, color, and hairiness. Hence, this review study aims to provide a thorough overview of the DL classification and diagnosis of skin diseases. However, DL techniques in dermatology show promising results.