Skin disease detection and segmentation using dynamic graph cut algorithm and classification through Naive Bayes classifier

•This paper used Machine Learning (ML) algorithm to classify skin conditions.•This paper proposed a new framework model and a dynamic graph cut algorithm.•The basic technique used in this is for skin lesion followed by a classifier. The largest organ and the outer covering of the human body is the skin. With seven layers of it covering the other organs inside, skin is one of the important part to take care of. A skin condition is one which affects the integumentary system and that includes a wide variety of diseases including dermatoses. Classifications of these skin conditions are always a challenge for any medical practitioner and they look at the machine learning systems to assist them in predicting and classifying the skin conditions. This in turn will help to cure or at least reduce the effect. If the skin symptoms such as acne, cellulitis, candidiasis, varicella, scleroderma, fungal skin, psoriasis, inflamed skin condition, etc. are left without treatment in its initial stage, then they can effect in different health impediments leading to even death. Image partitioning is a method which supports with the skin disease detection. Any abnormal skin growth is referred to as skin lesion which could either be primary or secondary. Graph cut algorithms are debated and used in the literature for variety of purposes including image smoothing, image segmentation and other problems involving energy minimization as objective. In this work, we intend to use a novel dynamic graph cut algorithm for skin lesion segmentation followed by a probabilistic classifier called as Naïve Bayes classifier for skin disease classification purposes. We have used ISIC 2017 dataset for testing our proposed method and found that the results outperform many state of the art methods including FCN and SegNet by 6.5% and 8.7% respectively. This dataset is available at the International Skin Imaging Collaboration (ISIC) website for public study and experimentation. In terms of accuracy, we could achieve 94.3% for benign cases, 91.2% for melanoma and 92.9% for keratosis on this data set.