Wearable and Detection of Skin Cancer Analysis Using

Abstract

Skin cancer rates have been increasing for the past few decades. The risk factor is the direct exposure of skin lesions to UV radiation which causes various skin diseases. Skin cancers are most common disease and are deadly to the human. Early detection of skin cancer can be cured. With the latest technologies, early detection is possible. One of such technique is artificial intelligence. The dermoscopy image is given as input and it is processed for noise filtering and image enhancement. Then the image is segmented using thresholding. A cancerous skin has certain features and such features are extracted using feature extraction. These features are given as input to the neural network. The Neural network is used to classify whether it is cancerous or non-cancerous.

Introduction

Cancer which affects the skin is called skin cancer. Skin cancer is of two types malignant or benign form. Benign Melanoma is the appearance of moles on the skin it is not a deadly one. Malignant melanoma is the appearance bleeding sores. It is the deadliest form of all skin cancers. It arises from cancerous growth in pigmented skin lesion. If it is diagnosed at the right time, this disease is curable. But diagnosis is difficult. It needs sampling and laboratory tests. Through lymphatic system or blood melanoma can spread to all parts of the body. So automatic detection will be useful at these cases. Basically skin disease diagnosis depends on the different characteristics like color, shape, texture etc. there are no accepted treatment for skin diseases Different physicians will treat differently for same symptoms. Key factor in skin diseases treatment is early detection further treatment reliable on the early detection. In this paper, Proposed system is used for the diagnosis multiple skin disease using artificial intelligence and neural network. This paper is organized as follows: Section I gives the introduction about Skin cancer and features of skin cancers. Section II describes the Automatic Skin cancer Detection system and various steps involved in the system. Section III gives the explanation of various algorithms used yet. Section IV describes about the proposed system. Section V concludes the paper followed by references.

Automatic skin cancer detection

Image acquisition

The first stage of any system is the acquisition of input image after the input image is obtained various process can be done on it to obtain the desired output here we do for image pre-processing and segmentation. CMOS camera is used as the medium for the acquisition of input image.

Noise filtering

Noise filtering is the process of removing noise from an image. Noise can be random or white noise with no coherence, or coherent noise introduced by the device's mechanism or processing algorithms. By doing this process we can obtain a good quality of image for the further segmentation.

Segmentation

This approach is a displaying system that takes in a practical mapping from an information picture to a yield picture. The information picture is the first picture, and the yield picture is a division cover. This empowers the system to show useful residuals, and additionally to supply higher determination data to the yield layers, so as to enhance execution of the system in contrast with systems without the skip associations. The exactness of the division procedure extraordinarily influences ensuing component extraction and order. Factors Concerning the Segmentation Various factors that affect the segmentation of skin cancer images are as follows:

• The skin lesions have complex structure, large variations in size as well as complex colours in the skin.
• The lesion is contrast to the surrounding skin.
• The borders of lesions are not always well defined.
• The influence of small structures, hairs, bubbles, light reflection, and other artifacts.
• The influence of the skin lesions in the surrounding regions.

Feature Extraction

The highlights which have been utilized to portray the skin sore pictures are depicted. In this work, we utilize shading, surface, and shading histogram highlights to speak to injury zones. The purpose of picking these sorts of highlights is a result of the way that shading and surface are the main properties commanding in the sore region. Feature extraction is the critical device which can be utilized to dissect and investigate the picture properly. They include extraction depends on the ABCD manage of dermatoscopy. The ABCD remains for Asymmetry, Border structure, Color variety and Diameter of sore. It characterizes the reason for the conclusion of a malady.

Classification

Injury grouping is the last advance. So as to arrange a picture grouping strategies like SVM method is used: Using support vector machine. Bolster Vector Machines depend on the idea of choice planes. A choice plane is otherwise called a hyper plane that isolates between arrangements of items having distinctive class enrollments. The isolating line characterizes a limit on the correct side of which all s are GREEN and to one side of which all items are RED. That is all focused on one side of the hyper plane are named yes, while the others are delegated no.

The algorithm of SVM classifier is given as:

1. Definition of Classification Classes - Contingent upon the goal and the qualities of the picture information, the order classes ought to be unmistakably characterized.
2. Selection of Features - Highlights to separate between the classes ought to be set up utilizing multispectral as well as multi-transient attributes, surfaces and so on.
3. Sampling of Training Data - Preparing information ought to be inspect keeping in mind the end goal to decide proper choice tenets.
4. Estimation of Universal Statistics - Different arrangement procedures will be contrasted and the preparation information, so that a suitable choice lead is chosen for ensuing grouping.
5. Classification - In light of the choice administer, all pixels are ordered in a solitary class. There are two techniques for pixel by pixel arrangement and per - field grouping, regarding divided zones.

Fuzzy logic

In fuzzy logic algorithm, a combination of both ABCD (Asymmetry, Boarder factor, Color factor, Diameter) rules and Wavelet coefficients has been used to improve the image feature classification accuracy In this, the percentage of red, blue, green is calculated using, Red% = [R÷ (B+G)] ×100 Blue% = [B÷(R+G)] ×100 Green% = [G÷ (B+R)] ×100 C1-RED, C1-BLUE, C1-GREEN is calculated here in order to determine if R/B/G is dominant over the other, Wavelet transform, Deconstruction, Reconstruction: The wavelet is repeated as, W (j) = W (j+1) + U (j+1) Fuzzy interference decision system will give us quantitative information about ABCD factors which is used with fuzzy interference system further. Accuracy is 60% only. B.K-Nearest Neighbour KNN remains for k-closest neighbour calculation; it is one of the easiest yet generally utilized machines learning calculation. A protest is ordered by the distance from its neighbours with the question being doled out to the class most basic among its k separate closest neighbours. On the off chance that k = 1, the calculation just turns out to be closest neighbor calculation, what's more the protest is characterized to the class of its closest neighbour.

The downside of k closest neighbours classifier is, it is influenced by the quantities of features. The result might be because of the solver whose undertaking in little component space is harder than in bigger ones. Truth be told, as the dimensionality expands then the arrangement issue turns out to be all the more directly detachable, which tends to facilitate the assignment of finding a legitimate isolating hyper plane. Hence, the preparation time will be longer when compared to SVM.

Artificial Neural Network

An Artificial Neural Network (ANN) is a data handling that is roused incidentally organic sensory systems, for example, the mind, process the data. An ANN is arranged for a particular application, for example, design acknowledgement or information order, through a learning procedure. A prepared neural system can be thought of as a 'specialist' in the class of data it has been given to break down. In case of a medical field, error rates of ANN were high when compared to SVM in which 82.7% test set correctness has been achieved.

Support Vector Machine

SVMs are presently a hotly debated issue in the machine learning group, making a comparative eagerness at the minute as Artificial Neural Networks used to do some time recently. Far being, SVMs yet speak to an effective method for general (nonlinear) grouping, relapse and anomaly discovery with a natural model portrayal. Bolster vector machines are an arrangement of related regulated learning strategies utilized for grouping and relapse. Given an arrangement of preparing cases, each set apart as having a place with one of two classifications, a SVM preparing calculation assembles a model that predicts whether another illustration falls into one classification or then again the other. So, when compared to all above methods, SVM is good to go.

Prorposed Method - Proposed block

In this project we have designed a diagnosis system based on the techniques of image processing. This work is done on different skin patterns and tones of images and it is analyzed to obtain the result whether the person is suffering from skin cancer or not. This system helps in the early detection and cure of skin cancer .this is cost effective and feasible test method for the detection of skin cancer. The below mentioned is the block of the early detection skin cancer analyzer.

Colour image to gray scale

As the skin tone of people may differ, based on their region of living this may affect the efficiency in the output .so in our project we convert the image to gray scale image to increase the efficiency of the output. B).Image restoration Image Restoration is the process of recovering the degraded image from a blurred and noisy one. The degraded images can be stored in different ways. Such as imperfection of imaging system, bad focusing, motion and etc are the various defects which cause image degradation. The corrupted images lead to fault detection, therefore, to select the most appropriate denoising algorithm it is essential to know about noises present in an image. The image noises can be divided into four groups of Gaussian, Salt and Pepper, Poisson and Speckle. The sample of such noises has been shown below,

1. Image without noise
2. Gaussian noise
3. Poison noise
4. Salt and Pepper noise
5. Speckle noise

Mean filters: It works best with Gaussian noise and for salt and pepper noise. Although this filter reduces the noise, blur the image and reduce sharp edges.

• Arithmetic mean filter: It is the simplest of mean filter. It can uniform the noise and works well with Gaussian noise.
• Geometric mean filter: It can preserve the detail information of an image better than the arithmetic mean filter
• Harmonic mean filter: It works well with salt noise, and other types of noise such as Gaussian noise, but doesn’t Work well with pepper noise.
• Contra harmonic mean filter: It can preserve the edge and remove noise much better than arithmetic mean filter. Because of preserving the edges character we use harmonic and contra harmonic filters in this system.

Removing Thick Hairs

Though the and skin lines such as rashes, moles will be smoothed using restoration filters, the image may include the hairs. Thick hairs in automated analysis of small skin lesions are considered to mislead the segmentation process. To remove the thick hairs in skin cancer images, methods such as mathematical morphology methods, curvilinear structure detection, and automated software called Dull Razor and Top Hat transform combined with a bicubic interpolation approach are preferred. The hair-free images are acquired using these operations.

1. Filtered image
2. Segmented image

Image enhancement

Histogram equalization the technique of adjusting image intensities for enhancing the contrast. It is one of the non-linear contrast enhancement technique. Let f be a given image represented as mr / mc matrix of integer pixel intensities ranging from 0 to L − 1. L is the number of possible intensity values. Often it will be 256. p is the normalized histogram of f with a bin for each possible intensity.

Edge detection

Feature extraction

Feature extraction is done using the properties called ABCDE in automated diagnosis of skin cancer. ABCDE represents Asymmetry, Border, Colour variation, Diameter and Evolution. Asymmetry: Asymmetric nature of melanoma is property in which the imaginary line passing through middle of lesion, either up or down or side to side gives two unequal or two non-symmetric parts. Degree of asymmetry can be calculated by using asymmetric Index which is calculated by using the formula, AI = (∆A/A) × 100, where A is the total area of the image and ΔA is the difference in area between total image and lesion area. Border irregularity: The border or edge of the skin cancer affected area will be usually blurred or ragged or irregular or notched. Border irregularity is usually calculated by compact index in medical image processing. Compact index is used to estimate unanimous 2D objects.

The measure is sensitive to noise along the boundary. Compact index is calculated using the formula, CI= (pl*pl)/4πAl where Pl is Perimeter of the Lesion and Al is area of the Lesion. Colour variation: Emergence in colour variation can be detected if lesion is melanoma. The colours can be variations in black, brown and red depending on the production of melanin pigment in the affected area. Colour variation can be detected statically and by plotting histograms of the segmented image. The intensity variation is high if there are colour variations. Diameter: Skin cancer (melanoma) usually have diameter more than 6mm. Since diameter is irregular, it is calculated by drawing from edge pixels to pixels in the midpoint and averaged.

Conclusion

A computer based early detection of skin cancer analyzer system is being proposed. It has been found to be a better diagnosis method than the artificial and k-nearest neighbor methods. This methodology uses image processing and support vector machine for classification of malignant melanoma from other skin diseases. Dermoscopic image were collected and processed by various image processing techniques. The cancerous region is separated from the healthy skin by the method of segmentation. Based the features the images are classified as cancerous or non- cancerous. It has got good accuracy and efficiency of 98% also. By further varying the image processing techniques and classifiers accuracy and efficiency can be improved for this system.