Improved Deep Learning Framework for Segmenting and Classifying Skin Lesions using K-nearest neighbor algorithm and Multi-Instance Learning

The biggest organ that is present in the human body, which generally covers all the major body parts.The functions of skin in the human body are more significant, since even a little alteration in one of these functions could have an impact on other body components. Skin is more susceptible to disease and infection because it is exposed to the outside environment. Therefore, we must pay more attention to skin diseases. Lesion areas refer to the affected area of skin. The initial clinical indications of diseases like chickenpox, melanoma, etc. are skin lesions. Today, computer-aided diagnosis is more prevalent in the medical industry. For the automation of many operations in the medical profession, machine learning is crucial. It has been shown that dermoscopy, in the hands of untrained dermatologists, may actually reduce the diagnostic accuracy. There are other illnesses that are equally important in the medical community. Therefore, in addition to the diseases stated above, we also take into account two other diseases, bullae and shingles. The characteristics utilized to characterize the lesion locations are an additional crucial factor. For this, we take into account aspects of color and texture. The learning algorithms are a further topic that needs to be considered. Although there are many classifiers in use, fusion of the results of different classifiers is rapidly gaining prominence since it helps improve categorization. The final outcome the author wants is to use both the algorithms and find a novel accuracy and comapare both of them.