International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 06 | Jun 2025
p-ISSN: 2395-0072
www.irjet.net
Skin Disease Prediction using Dynamic Testing in Machine Learning Mr. N. L. Bhale¹, Prasad Navale², Om Jadhav³, Sakshi Dobhal´, Rohit Jadhavµ 1 Head of Department, Department of Information Technology, Matoshri College of Engineering & Research
Centre, Eklahare, Maharashtra, India
2,3,4,5 Students, Department of Information Technology, Matoshri College of Engineering & Research Centre,
Eklahare, Maharashtra, India ---------------------------------------------------------------------***--------------------------------------------------------------------1.1 MOTIVATION Abstract - Skin diseases are prevalent worldwide, affecting millions of people and often presenting diagnostic challenges due to their diverse manifestations. Deep learning techniques have shown promise in automating the diagnosis of skin diseases, leveraging large datasets and powerful computational methods. However, the performance of existing models may vary depending on factors such as dataset quality, model architecture, and feature extraction methods.
Skin diseases pose a significant burden on public health globally, affecting millions of individuals and often requiring timely and accurate diagnosis for effective management. Despite advances in medical imaging and diagnostic techniques, the complexity and variability of dermatological conditions present challenges for clinicians in accurately identifying and treating these diseases. Traditional diagnostic methods rely heavily on visual inspection and subjective assessment, which can lead to variability in diagnoses and delays in treatment initiation.
Key Words: Convolution neural network, combined decision, deep learning, skin cancer.
Temporal Dynamics of Skin Lesions: Skin diseases often exhibit dynamic changes over time, including progression, regression, and response to treatment. By analyzing sequential images captured at different time points, we can gain valuable insights into the temporal evolution of skin lesions, which may provide important diagnostic and prognostic information.
1. INTRODUCTION Skin diseases are a significant public health concern worldwide, affecting individuals of all ages and demographics. The diagnosis and management of these conditions often pose challenges due to the wide variety of dermatol(CNNs) have been particularly effective in analyzing staogical manifestations and the need for accurate and timely assessment. While traditional diagnostic methods rely heavily on clinical expertise and histopathological examination, the advent of artificial intelligence (AI) and deep learning techniques has revolutionized the field of dermatology by enabling automated and efficient skin disease diagnosis.
Improved Diagnostic Accuracy: Conventional static image- based approaches may overlook critical temporal patterns and dynamics present in skin lesions. Dynamic testing in deep learning allows us to leverage sequential data to capture these temporal dynamics, potentially leading to more accurate and reliable disease predictions compared to static image analysis alone.
Deep learning, a subset of AI, has shown remarkable success in various medical imaging tasks, including dermatology. Convolutional neural networks tic images of skin lesions, achieving performance comparable to or even surpassing that of dermatologists in certain scenarios. However, conventional static image-based approaches may overlook crucial temporal information inherent in the evolution of skin diseases over time.
1.2 Proposed System The purpose of the proposed system, "Skin Disease Prediction using Dynamic Testing in Deep Learning," is to develop an advanced computational tool that leverages the temporal dynamics of skin lesions to enhance the accuracy, efficiency, and automation of skin disease diagnosis and prediction. The system aims to address the following key objectives:
Dynamic testing in deep learning offers a promising solution to this limitation by leveraging sequential data to capture temporal dynamics in skin lesions. Unlike static image analysis, dynamic testing involves the analysis of sequential images taken at different time points, enabling the model to learn from the progression or regression of skin diseases. This temporal perspective can provide valuable insights for disease prediction, monitoring, and treatment response assessment.
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Improved Diagnostic Accuracy: By analyzing sequential images of skin lesions captured at different time points, the system seeks to capture temporal patterns and dynamics that may contain valuable diagnostic information. By incorporating temporal information into the prediction process, the system aims to improve the accuracy of skin disease diagnosis
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