Early Detection of Skin Diseases using Machine learning and Deep Learning

International Research Journal of Engineering and Technology (IRJET)

e-ISSN: 2395-0056

Volume: 11 Issue: 04 | Apr 2024

p-ISSN: 2395-0072

www.irjet.net

Early Detection of Skin Diseases using Machine learning and Deep Learning B.Harshitha

E. Krishna Rao Patro

B.Padmaja

E.Amrutha Varshini

Department of Computer science and Engineering Institute of Aeronautical Engineering,Hyderabad Telangana

P.Rajesh Department of Computer science and Engineering Institute of Aeronautical Engineering,Hyderabad Telangana -----------------------------------------------------------------------***-------------------------------------------------------------------ABSTRACT— In this work, we propose a groundbreaking approach to address the challenges posed by conventional methods in diagnosing skin issues, particularly in dermoscopic image analysis. Skin problems affect millions worldwide, impacting their well-being and incurring significant medical expenses. Timely diagnosis is essential for effective treatment, yet existing techniques are often limited due to the diverse visual characteristics and overlapping symptoms of various skin conditions. To tackle this issue, we introduce a novel methodology that combines dermoscopic images with artificial intelligence to develop an autonomous diagnosis system capable of identifying multiple types of skin lesions. Early detection is key to preventing the progression of skin conditions, and our approach aims to provide a more accurate and efficient means of diagnosis.Utilizing deep learning techniques integrated with a computer-aided diagnosis system, our research seeks to revolutionize the recognition of skin problems. Actinic keratoses, Benign keratosis, Melanocytic nevi, Basal cell carcinoma, Dermatofibroma, Melanoma, and Vascular skin lesions are among the targeted illnesses. By leveraging advanced technology, such as imaging devices and routine skin examinations, we aim to enhance early detection efforts and improve overall health outcomes.The proposed methodology addresses the shortcomings of traditional diagnostic procedures, offering a promising solution for early diagnosis and intervention. With an impressive accuracy rate of 89%, our research demonstrates the effectiveness of our approach in accurately identifying and classifying various skin lesions.

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Impact Factor value: 8.226

This breakthrough has the potential to significantly impact dermatological diagnostics, paving the way for improved patient care and outcomes in dermatology. Keywords— Dermoscopic, skin issues, timely diagnosis, autonomous diagnosis system, deep learning techniques, computer-aided diagnosis, actinic keratoses, benign keratosis, melanocytic nevi, basal cell carcinoma, dermatofibroma, melanoma, vascular skin lesions, early detection, improved patient care

I. INTRODUCTION Skin disorders affect millions of people globally, which emphasizes the critical requirement of an accurate and timely diagnosis to enable effective treatment and prevent future issues. Dermatologists often find it difficult to identify skin conditions accurately even with their extensive training because many ailments have similar symptoms and features. Combining deep learning with computer vision has shown promise in the past several years for automating the detection of skin problems. In particular, two methods utilized in hybrid deep learning models—recurrent neural networks (RNNs) and convolutional neural networks (CNNs)—have proven remarkably successful at categorizing skin images into different disease classes. The intersection of deep learning and computer vision has revolutionized the diagnosis of skin diseases, especially in the field of dermatology. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are two examples of hybrid deep learning models, which are a relatively new idea

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