2 research outputs found

    A Review on Skin Disease Classification and Detection Using Deep Learning Techniques

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    Skin cancer ranks among the most dangerous cancers. Skin cancers are commonly referred to as Melanoma. Melanoma is brought on by genetic faults or mutations on the skin, which are caused by Unrepaired Deoxyribonucleic Acid (DNA) in skin cells. It is essential to detect skin cancer in its infancy phase since it is more curable in its initial phases. Skin cancer typically progresses to other regions of the body. Owing to the disease's increased frequency, high mortality rate, and prohibitively high cost of medical treatments, early diagnosis of skin cancer signs is crucial. Due to the fact that how hazardous these disorders are, scholars have developed a number of early-detection techniques for melanoma. Lesion characteristics such as symmetry, colour, size, shape, and others are often utilised to detect skin cancer and distinguish benign skin cancer from melanoma. An in-depth investigation of deep learning techniques for melanoma's early detection is provided in this study. This study discusses the traditional feature extraction-based machine learning approaches for the segmentation and classification of skin lesions. Comparison-oriented research has been conducted to demonstrate the significance of various deep learning-based segmentation and classification approaches

    New Opportunities and Challenges for Health Professionals in the era of Artificial Intelligence – Review

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    Introduction and purpose: Modern medical knowledge has grown to a vastness incomprehensible for a single health professional to learn and accommodate. The usage of modern information technologies comes to help, one of them being artificial intelligence, a branch of computer science aimed at developing solutions to perform tasks similar to the human brain, but more efficient and complex, without actual human intervention.  The goal of this review is to provide reader with the knowledge how artificial intelligence is applied in various branches of medicine. Brief description of the state of knowledge: In the fields of infectious diseases, including COVID-19 diagnostics, radiology, dermatology and surgery, works lean toward the statement, which suspect application of AI is beneficial for medical practitioners. Programs help to develop statistical models for virus spreading and the creation of antiviral solutions. The radiological application involves the analysis of images to aid radiologists in diagnosing certain features, similarly to dermatology, where eg. AI can identify malignancy of skin nevi. In the department of surgery, predictive algorithms can help in choosing operation methods and improve outcomes. Conclusions: Usage of AI assistance in the medical field has proven to be successful, but it is yet to be commonly encountered in everyday work. Programs need to be further developed and made more approachable to users without expertise in the IT field. AI may also prove useful in the process of education of health professionals
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