Advanced Skin Diseases Diagnosis leveraging image Processing

ABSTARCT :

Air pollution affects human skin in many ways. Skin diseases are common in densely populated regions. These diseases have a devastating impact on people's lives by creating a huge need for the disease diagnosis. The proposed work on skin disease determination system aims for an accurate diagnosis leveraging image processing. The methodology outlined here aims to identify skin disease by scrutinizing the input image. The method involves filtering of the input provided to remove noise. Conversion of image to a grayscale image, and image segmentation. Feature extraction is used to minimize the amount of data to be processed by the classifier. The SVM (Support Vector Machine) is then used in the image classification to identify the skin disease. The increased use of technology has led to an efficient and accurate way of diagnosis that aids in curing the disease more expeditiously. Using the proposed method skin diseases such as rosacea, melanoma, psoriasis and acne are identified with a high accuracy of 89%.

EXISTING SYSTEM :

Skin diseases such as Melanoma and Carcinoma are often quite hard to detect at an early stage and it is even harder to classify them separately. Recently, it is well known that, the most dangerous form of skin cancer among the other types of skin cancer is melanoma because it is much more likely to spread to other parts of the body if not diagnosed and treated early. In order to classify these skin diseases, “Support Vector Machine (SVM)” a Machine Learning Algorithm can be used.

DISADVANTAGE :

For a long time, algorithms for finding objects in images have been studied extensively. Numerous researches have been done in object detection, recognition, classification and discrimination.

PROPOSED SYSTEM :

In this paper, we propose a method to identify whether a given sample is affected with Melanoma or not. The steps involved in this study are collecting labeled data of images that are pre-processed, flattening those images and getting the pixel intensities of images into an array, appending all such arrays into a database, training the SVM with labeled data using a suitable kernel, and using the trained data to classify the samples successfully. The results show that the achieved accuracy of classification is about 90%.

ADVANTAGE :

Experiments show that analyzing the shadow of the skin lesions can effectively detect and classify the lesion of concern.