Feature Set Clustering and Classification for Melanoma Detection using Enhanced K Nearest Neighbour
Keywords:
Feature Set, Clustering, Classification, Melanoma Detection, K Nearest Neighbour, Early Detection, Prediction AccuracyAbstract
A correct diagnosis is one of the most important parts of medical care. In terms of diagnostic instability and difficulty, dermatology ranks high among medical specialties. In order to confirm a correct diagnosis, dermatologists frequently ask for further information, such as the patient's medical history and results of additional tests. Consequently, it is critical to discover a way that can ensure a correct and trustworthy diagnosis in a timely manner. Over the years, a number of methods have been created to aid in the machine learning-based diagnosis. But there are features, like great accuracy, that the traditional systems don't have. The skin is the primary organ of the human body, and skin cancer is the most common form that affects a large number of people annually. The goal of current research is to lower the death rate from skin cancer. It is simple to cure malignant melanoma when detected in its early stages. The early stages of extinction suggest a high chance of survival, thus prompt diagnosis is essential. This research describes a system for clinical decision-making that uses a image set of the area of the skin that needs to be diagnosed as input for the diagnosis of melanoma. The system determines the features that reflect the extent of damage by analyzing an image sequence to identify the afflicted area. Based on these features, the system produces a determination. The process of creating a model of classification for the accurate identification of melanoma that is malignant, a severe form of skin cancer, is discussed in this work. This research proposes a Feature Set Clustering and Classification for Melanoma Detection using Enhanced K Nearest Neighbour (FSCC-MD-EKNN) for accurate classification of melanoma. The proposed model when compared with the existing models achieves 98.4% accuracy in classification.
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