International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 07 | July 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 1331 Detection and Classification of Skin Diseases using Different Color Phase Models A.V.Ubale 1 , P.L. Paikrao 2 1 Goverment college of Engineering, Amravati, India ---------------------------------------------------------------------***---------------------------------------------------------------------- Abstract - Now a days, skin diseases are mostly found in animals, humans and plants. A skin disease is a particular kind of illness caused by bacteria or an infection. These diseases like Psoriasis, Melanoma, Papillomus, Mycosis, Warts etc. have various dangerous effects on the skin and keep on spreading over time. It becomes important to identify these diseases at their initial stage to control it from spreading. These diseases are identified by using many technologies such as image processing, data mining, k nearest neighbor (KNN) etc. Recently, image processing has played a major role in this area of research and has widely used for the detection of skin diseases. Techniques like filtering, segmentation, feature extraction, image pre-processing and edge detection etc. are part of image processing and are used to identify the part affected by disease, the form of affected area, its affected area color etc. This thesis presents a various skin disease detection and classification systems using image processing techniques in recent times. A comprehensive study of a number of skin disease diagnosis systems are done in this thesis, with different methodologies and their performances. Key Words: skin diseases; skin infections; image processing; lesions; histograms etc. 1. INTRODUCTION AND LITERATURE REVIEW The biggest organ of the body is human skin. Its weight lies between six and nine pounds and surface area is about two square yards. Inner part of body is separated by skin from the outer environment. It provides protection against fungal infection, bacteria, allergy, viruses and controls temperature of body. Situations that frustrate, change texture of the skin, or damage the skin can produce symptoms like swelling, burning, redness and itching. Allergies, irritants, genetic structure, and particular diseases and immune system related problems can produce dermatitis, hives, and other skin problems. Many of the skin diseases, such as acne, alopecia, ringworm, eczema also affect your look. Skin can also produce many types of cancers. Image processing is used to detect these diseases by using various methods like segmentation, filtering, feature extraction etc. To get an improved image or to get meaningful information from an image, it is necessary to convert an image into digital form and then perform functions onto that image. It is a part of signal processing. The input is an image and it may be a video, a photograph and output is also another image having same characteristics as input image. Here is a survey on image processing Techniques: In Nikos Petrellis [1] is proposed a method in which quantitative analysis is done. Three step process is done in which first step is image enhancement, second process image segmentation, feature extraction is a third step and then forth step is classification of skin diseases. 1. In this method they used MATLAB software for Image enhancement. 2. They used a advanced algorithms for the image segmentation. 3. By using co-occurrence matrix, features can be extracted for the classification. 4. Supervised Vector Machine(SVM), Neural Network(NN) are used for the classification and also they some deterministic method related to k- Nearest Neighbours or Decision trees is adopted for classification of images. H. Mirzaalian, T. K. Lee and G. Hamarneh [2] proposed the matching process, for facilitate special normalization they used human back template (atlas). On 56 pairs of dermatological images they applied point- matching algorithms and by using this spatially normalized coordinates, accuracy of PSL matching get improved. R. Yasir, A. Rahman, and N. Ahmed [3] proposed method by which they detect various skin diseases by using computers vision techniques. for feature extraction, they used many image processing algorithms and for testing and training purposes they used feed forward artificial neural network. This method carried out into two phases one for feature extraction and another for identification of diseases By this method, they detect 9 types of diseases with 90 percent accuracy. P. S. Ambad, A. S. Shirsat [4] proposed a method which used multiple skin disease for diagnosis for analysis.. They classified this images using two level classifiers for improve the result. Basically AdaBoost classifier gives better result which correlate the images with respect to the parameters. P. B. Manoorkar, Prof. D. K. Kamat, Dr. P. M. Patil [5] used Bioimpedance measurement method for analysis and