International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 04 | Apr 2025
p-ISSN: 2395-0072
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Radiant Dermat: Skin dermatology tool using Computer Vision and ML Sonal Chaudhari¹, Aaryaman Ahirao², Jivhesh Chaudhari³, Vinay Kumkar´, Aditya Kelaskarµ ¹Assistant Professor, Dept. of Computer Engineering, Datta Meghe College of Engineering, Navi Mumbai, India ²³´µDept. of Computer Engineering, Datta Meghe College of Engineering, Navi Mumbai, India ----------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - The project focuses on the development of an
serves as a valuable tool that augments the capabilities of dermatologists, particularly in the early detection of melanoma, which can significantly improve patient survival rates [2] . The capabilities of AI in this domain include providing diagnostic support, facilitating medical interventions, and aiding in the development of proactive health strategies . Many modern hospitals are increasingly incorporating AI technologies to reduce operational costs and enhance diagnostic precision [2] . These systems can rapidly analyze large volumes of medical data, identifying patterns and anomalies that may elude human detection, thus streamlining workflows and ensuring more accurate diagnoses.
Android-based application for disease detection using realtime image capture and image uploads. The app employs a machine learning model that utilizes advanced technologies such as deep learning, vision transformers, Swin Transformer technology, image segmentation, an encoderdecoder unit, and a SoftMax probabilistic classifier. Key Words: Aritificial Intelligence, Convolutional Neural Network.
Deep
learning,
1.INTRODUCTION Radiant Dermat is an advanced skin diagnosis tool that leverages the power of machine learning and computer vision to provide accurate and personalized skin assessments. This innovative platform aims to revolutionize the field of dermatology by offering accessible and efficient skin healthcare solutions. Using cutting-edge image analysis and deep learning algorithms, Radiant Dermat can identify skin lesions and provide customized recommendations to users. This project aims to achieve the highest level of accuracy and reliability for the end users. By harnessing the power of machine learning (ML) and computer vision, this innovative platform offers accurate, personalized skin assessments, making expert-level dermatological evaluations accessible to anyone, anywhere.
2.2 MOBILE APPLICATIONS FOR SKIN LESION DETECTION The development and interest in smartphone applications for skin lesion diagnosis and triage are on the rise [1] . The widespread use of smartphones has made it feasible to develop AI models that can run directly on patients' devices, offering a convenient and private means for initial skin assessments [3] . This on-device processing of AI models addresses data privacy concerns by ensuring that sensitive medical data remains on the user's device, rather than being transmitted to external servers for analysis [3] . However, while some mobile skin lesion analysis applications demonstrate promising accuracy in controlled settings, many face limitations in terms of sensitivity and specificity when evaluated in real-world scenarios [4] . Clinical validation of these apps remains a significant challenge, with studies revealing variable and often low accuracy, recommending caution against their sole use for diagnostic purposes [5]. For instance, a 2020 systematic review of several melanoma detection apps found poor study design and a high risk of bias, with many apps failing to accurately identify melanoma cases [5]. Conversely, some applications, like Dermalyser, have shown high diagnostic accuracy in prospective clinical trials, indicating the potential of AI in this domain when rigorously validated [6] . The performance of AI-powered skin lesion detection apps is also significantly influenced by the quality of the input images . Standardized image capture and high-quality images are crucial for ensuring the reliability of AI analysis, as variations in lighting, focus, and angle can affect the accuracy of the results [1].
2. LITEARTURE SURVEY 2.1 INFLUENCE OF AI IN DERMATOLOGY The application of AI within dermatology is experiencing a period of rapid growth, extending across a broad spectrum of concerns from common skin conditions like acne and eczema to the more critical areas of skin aging and skin cancer detection [1] . This technological advancement addresses a crucial public health issue by enhancing diagnostic accuracy, efficiency, and accessibility [2] . AI is transforming dermatology by automating many aspects of diagnostic processes into digital systems, thereby reducing manual administrative tasks and allowing healthcare professionals to focus on more critical medical decisions [2] . This shift is particularly evident in early skin cancer detection, where AI-based systems leverage medical data and analytics to improve diagnostic processes and simplify medical services . Furthermore, AI
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