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It is vital in today's technologically advanced society to combat skin cancer using machines rather than human intervention. Any time the look of the skin changes abnormally, there is a danger that the person might be at risk for skin cancer. Dermatology expertise and computer vision methods must be merged to diagnose melanoma more effectively. Because of this, it is necessary to learn about numerous detection methods to help doctors discover skin cancer at an early stage. This research paper provides a comprehensive technical review of the advancements in using deep learning techniques for the diagnosis of skin cancer. Since skin cancer is so prevalent, early identification is essential for better treatment results. Among the medical uses where deep learning, a kind of machine learning, has shown promise is in the identification of skin cancer. This research investigates the most cutting-edge skin cancer diagnostic deep-learning approaches, datasets, and assessment metrics currently in use. This study discusses the benefits and drawbacks of using deep learning for skin cancer detection. Challenges include ethical and privacy considerations about patient data, the incorporation of models into clinical procedures, and problems with dataset bias and generalisation. [ABSTRACT FROM AUTHOR]

Today, the general situation worldwide is that the hospitals, sanatoriums and medical colleges are running out of beds, oxygen, medical staff, ventilators and other required paraphernalia that is mandatory for the treatment of the vicious pandemic [1]. The requirement is for a system that takes in some input parameters like Oxygen level of the patient, pulse rate and respiration rate and in turn predicts the Life Risk Rate of that patient [2]. The model used here is a fuzzy regression model that gives the prediction of Life Risk Rate between 1 and 10 units. The lower the predicted Life Risk Rate, the better the chances of survival of the Covid patient. But if the predicted Life Risk Rate is more than the mean of the observations of the Risk in the dataset, then immediate emergency is needed. The benefit of this system is that the patients requiring immediate admission and treatment can be filtered and medical aid in hospital be thereby provided for critical patients. Rest may be home quarantined and domestic medical aid may be given to them until in some unfortunate situation their Risk Rate is near alarming. This paper aims to provide some help in this crucial situation. [ABSTRACT FROM AUTHOR]