International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 10 Issue: 05 | May 2023
p-ISSN: 2395-0072
www.irjet.net
Skin disease detection and classification using different segmentation and classification techniques Samiksha Prachand1, Nisha Patil2, Neha Badoge3, Vaibhavi Chaudhari4, Bhargavi Kaslikar5 1,2,3,4Student, Final year B. Tech, Electronics Engineering, K.J. Somaiya College of Engineering, Mumbai,
Maharashtra, India
5Professor, Electronics Department, Somaiya College of Engineering, Vidyavihar, Mumbai, Maharashtra, India
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Abstract - Skin diseases are common and affect a large
is a pigment made by melanocytes that give skin its color and shield it from the sun's UV rays.
population all over the world. Skin disease management necessitates accurate diagnosis and treatment. The systems used image processing and machine learning techniques to automate the process of identifying skin diseases. With the advancement of machine learning algorithms, automated systems for skin disease detection and classification are now possible. This paper presents a study and exploration of various segmentation techniques used to detect the type of skin lesions, including Region-based segmentation, Otsu's Thresholding, Boundary, and spot detection, and Entropybased segmentation. Furthermore, support vector machines, Decision Trees, Random Forests have been used to classify skin diseases. Melanotic nevi, Melanoma Benign, Keratosis, Basal cell Carcinoma, Actinic Keratosis, Vascular Lesions, and Dermatofibroma are the seven types of skin cancer. The primary goal of this project is to improve diagnostic system accuracy by utilizing image segmentation and classification techniques.
The dermis is the skin's intermediate layer and contains connective tissue, blood vessels, nerves, and other structures. It provides structural support to the skin and contains collagen and elastin fibers, which impart elasticity and strength to the skin. There are also hair follicles, perspiration glands, and sebaceous glands. Fat and connective tissue make up the subcutaneous tissue, the skin's deepest layer. It Insulates and regulates body temperature. Although cancer can manifest itself in any of the skin's layers, basal cell carcinoma (BCC), the most prevalent form of skin cancer, is most often found in the epidermis' basal cells. The epidermis's base is home to a special type of cell called a basal cell. The epidermis' outermost layers are frequently the starting point for squamous cell carcinoma (SCC), another frequently encountered form of skin cancer. Melanoma, a more aggressive form of skin cancer, develops in the melanocytes, which are in the epidermis's deeper layers. Depending on their form and cause, skin lesions can also affect various layers of skin. For instance, psoriasis lesions, which are characterized by thick, scaly regions of skin, affect the epidermis, and can extend into the dermis. In severe cases, the subcutaneous tissue may be affected by eczema lesions, which are caused by inflammation and irritation of the skin.
Key Words: Skin Disease Classification, Convolution Neural Network, Deep Learning, Classification Algorithms, Image Processing.
1. INTRODUCTION The early diagnosis and treatment of skin illnesses depend greatly on the detection and classification of skin diseases, which is a critical responsibility in dermatology. Skin conditions are common around the world, and early diagnosis and classification can prevent death and lessen the financial load on the healthcare system. Recent advancements in machine learning and computer vision have led to the development of automated systems for skin disease detection and classification, which can assist dermatologists in providing accurate diagnoses and individualized treatments.
The purpose of this work is to provide a comprehensive analysis of existing methods and algorithms for detecting and classifying skin diseases. The HAM10000 dataset, which contains photos of skin lesions, will be used to test the effectiveness of various methods and algorithms. We hope that the work presented here will help move the field of skin disease identification and categorization forward and ultimately improve the lives of many people around the world.
The epidermis, dermis, and subcutaneous tissue make up the skin, which is the largest organ in the human body. The epidermis is the skin's outermost layer and a barrier against environmental hazards. Layers of keratinocytes, melanocytes, and Langerhans cells make up its structure. Keratin, which is synthesized by the skin's keratinocytes, gives the skin its strength and ability to repel water. Melanin
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In this work, we employ the publicly available HAM10000 dataset for machine learning. Ten thousand and fifteen dermatoscopy photographs of pigmented skin lesions are included, with seven thousand two hundred and ninety-five representing benign lesions and two thousand depicting
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