Lesion Interpretation System Using Image Processing

Authors: H Meenal, V Swapnika, M Partha Saradhi, S Vinay

DOI Link: https://doi.org/10.22214/ijraset.2026.79176

Abstract

This Due to the significant visual similarities between different lesion pattern types and the inconsistent quality of photographs taken in real-world settings, accurately identifying dermatological abnormalities continues to be a significant difficulty. Automated preliminary assessment is becoming more important due to the growing requirement for intelligent support systems that are both efficient and effective. The framework for an integrated method of analyzing and interpreting lesion images using deep learning and image processing is put forward. To enhance the quality and consistency of the analysis, the input image undergoes preprocessing steps such illumination evaluation, scaling, and normalizing. A convolutional neural network is then employed to obtain complex representations and classify the images for various lesion types. The interpretation ability of the output results is enhanced by incorporating an interpretation layer to provide descriptive results and precautionary advice. In addition, a threshold-based segmentation approach is employed to determine the spread of the lesion, thereby allowing for an indication of the severity in a simplified manner. The suggested strategy shows the potential for an efficient and successful support system for early-stage.

Introduction

Medical image analysis has evolved from traditional rule-based image processing to machine learning and deep learning, particularly convolutional neural networks (CNNs), which excel at recognizing complex lesion patterns. Dermatological image analysis faces challenges such as high similarity between lesions, lighting variability, and device differences, as well as limitations in interpretability and usability. To address these, a hybrid framework is proposed that combines image preprocessing, CNN-based multi-class classification, confidence calibration, lesion spread estimation, and integration of clinical knowledge. This system enhances robustness under varying imaging conditions, provides interpretable outputs including severity and symptoms, and offers decision support, making it more practical and user-friendly than previous segmentation-focused models.

Conclusion

The framework that is proposed will be helpful in providing a practical and efficient solution to automate the interpretation of the lesion. Instead of providing simple classification output from the system, the framework will be helpful in im-proving the functionality of the system, as it will involve the assessment of the quality of the image, spread-based analysis, and addition of information to the system. This will be helpful in improving the output of the system, as it will be more user-friendly. The results obtained from the system indicate that it is performing well even when the input is changed appropriately. This is because the system is properly preprocessed and analyzed, and the addition of confidence calibration will be helpful in improving the output, as it will provide a better understanding of the output. Similarly, the addition of spread-based analysis will be helpful in providing a better understanding. This framework can be effectively applied in preliminary analysis scenarios where simple and easily accessible results are necessary. Possible improvements may be made to the boundary detection mechanism by using advanced segmentation techniques, improving robustness to different imaging conditions, and extending the system to handle more categories. This framework has the potential to evolve into a more comprehensive assistive tool for intelligent lesion analysis

References

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Copyright

Copyright © 2026 H Meenal, V Swapnika, M Partha Saradhi, S Vinay. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Paper Id : IJRASET79176

Publish Date : 2026-03-31

ISSN : 2321-9653

Publisher Name : IJRASET

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