Predictive model for skin disease in animals E-Book

Utpal shrivastava

• Herausgeber: BookRix
• Kategorie: Bildung
• Sprache: Englisch

Veröffentlichungsjahr: 2019

Beschreibung

There exists various experts system for skin disease diagnosis by using image processing. These models were implemented only for humans and plants skin disease detection. No model has been implemented for disease diagnosis among the animals. Different image segmentation algorithms are in place, which vary for different types of images like disease affected image, medical image etc. There are many classification techniques, algorithms and classifiers are available to distinguish the data into various classes. Each algorithm has their own benefits and drawbacks. Here the proposed system has been implemented by using image processing for skin disease detection in animals. In this approach, two common disease i.e. Alopecia, Ringworm has been taken. This proposed system used k-means clustering algorithm for image segmentation and SVM classifier to do classification of data in two different classes. Experiments are performed on available data to measure the accuracy and effectiveness of system. The proposed system also measures the execution time for each and every image of disease. This is investigated that which features have large impact on the developed methodology. Experimental results show the effectiveness of this model.

Leseprobe

Utpal shrivastava

Predictive model for skin disease in animals

BookRix GmbH & Co. KG81371 Munich

Chapter 1

CHAPTER 1

INTRODUCTION

1.1 Skin Disease

The skin is a huge part of physical appearance. It provides protection against infection, bacteria, virus etc. and controls temperature of body. It is a complex active organ, if any of its functions fail there can be serious consequences. Skin disease is an impairment of health or condition of abnormal functioning. It is a particular kind of illness caused by bacteria, fungus, viruses. Problems can develop wherever there is skin, including the ears, around the lips, the bottom of paws and around the anus. Skin disorders vary greatly in symptoms and severity. Situations that frustrate, change texture of the skin, or damage the skin can produce symptoms like swelling, burning, redness and itching. Skin disease causes significant impact on a person having it, causing the person to feel discomfort in his/her daily routines or may cause the person to be disabled. Collectively, there are more than 3000 known skin diseases that are present today.

1.2 Types of Skin Disease

There are various types of skin diseases found in humans, plants, animals. These diseases can be classified according to their symptoms and features. They can be:

Temporary or permanentSituational or geneticPainless or painfulMinor or life-threatening

These are the diseases that may originate inside the body and manifest on the skin. Permanent skin disorders are those that are present from birth. Temporary skin diseases have various causes like environmental factors, inflammation. Now a day, skin diseases such as alopecia, ringworm, hot spot and yeast infection are very common to everyone. They tend to pass on from animal to animal or animal to human very easily. These

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diseases have various dangerous effects on the skin keep on spreading over time especially when it is on the part of the body that cannot be covered. Therefore it becomes important to control it at initial stage to prevent it from spreading. These diseases also have psychological effects on humans and animals. Common skin conditions include moles, hair loss, rubbing, redness, itching, rashes, circular patches, discolored and inflamed skin. Skin can also produce many types of cancers. These diseases don‟t just damage the skin. It can have a large impact on animals and humans daily life and stop their movement. The worst situation is that, it can even kill. Animal disease means a disease to which animals are liable and whereby the normal functions of any organ or the body of an animal is impaired or disturbed by any protozoon, bacterium and virus. These diseases are identified by using many technologies such as image processing, data mining, artificial neural network (ANN) etc.

1.3 Common Skin Disorders and Their Symptoms

Skin cancer: red, pink or rough patch of skin on sun-exposed areas.Lupus: fatigue, headaches, fever and painful joints, disc shaped rash that does not itch.Alopecia or hair loss: discolored skin, how much hair loss depends upon breed, time and environmental factors.Ringworm: redness, itching, circular shaped rash.Allergies: redness, itching, small acne onto skin, burning due to sun rays.Acne: small red raised bumps caused by infected hair follicles.Rosacea: flushing, acne-like breakouts.Eczema: yellow or white scaly patches, affected area may be red, itchy and greasy.

1.4 Cause of Skin Disease

The skin‟s ability to act as a barrier is particularly important for occupational health. The skin has four layers namely, base layer, prickle cell layer, granular layer, horny layer. Problems occur when skin barrier is breached.

Fig 1.1: Architecture of skin layers

This can happen due to:

A material penetrates the barrier layer or alters it so other materials or agents can penetrate it.A material or agent enters sweat ducts or hair follicles, by-passing the barrier layer.

1.5 Image Processing

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. Digital image processing is the manipulation of images by computers. It has many applications in areas such as telecommunication, medical

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imaging, graphic arts, remote sensing etc. It works to divide an image into pixels and work on an individual pixel. This tool gives more accurate result as compared to other techniques. One of main purpose of image processing is to manipulate pixel values for better visibility. For example, gray-level transformation and image filtering are techniques for converting an input image into a new image with better visibility. Another purpose is to extract some target objects or regions from an input image. For example, first extract all organelles of a specific kind, then count them and understand their distribution and behavior in a cell. Image pattern recognition is widely used to classify an image or region into group of similar type of objects i.e. called class.

1.6 What Does An Image Analysis System?