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 Classification Using Explainable AI Prof. Lakshminarayana P1, Ms. Chandana2 1Associate Professor, BMS College of Engineering, Dept of Computer Applications, Bengaluru 2 Student, BMS College of Engineering, Dept of Computer Applications, Bengaluru
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Abstract - One of the most prevalent and possibly fatal
survival. However, traditional diagnostic methods depend heavily on expert dermatologists and histopathological analysis, which can be time-consuming, subjective, and not always accessible, especially in rural or underdeveloped regions.
types of cancer in the world is skin cancer. Early and accurate diagnosis plays a critical role in effective treatment and improved survival rates. In this research, we present a deep learning-based skin cancer detection system utilizing dermatoscopic images from the publicly available ISIC dataset. The dataset was preprocessed using grayscale conversion, CLAHE for contrast enhancement, Gaussian blurring for noise reduction, and Canny edge detection with edge overlays to highlight critical features. To ensure class balance and improve model generalization, data augmentation techniques such as rotation, horizontal flipping, and zooming were applied. The dataset was then split into training (80%), validation (10%), and test (10%) sets.
With the advancement of Artificial Intelligence (AI) and Deep Learning (DL), computer-aided diagnosis (CAD) systems have gained popularity as powerful tools to support medical professionals in early skin cancer detection. In this project, we propose an automated deep learning-based system to classify various types of skin cancer using dermatoscopic images from the ISIC dataset. The dataset was enhanced through data balancing and augmentation techniques to address class imbalance and improve model performance. We applied several preprocessing steps, including grayscale conversion, CLAHE for contrast enhancement, Gaussian blur for noise reduction, and Canny edge detection with red overlay to emphasize the lesion boundaries. We trained and evaluated three state-of-the-art convolutional neural network (CNN) architectures—EfficientNet, MobileNet, and ResNet50—using TensorFlow and Keras. These models were optimized using the Adam optimizer and trained over 30 epochs.
We implemented and trained three deep learning models— EfficientNet, MobileNet, and ResNet50—using TensorFlow and Keras, with hyperparameter tuning and the Adam optimizer. Among the models, EfficientNet achieved the highest validation accuracy of 92.44%, followed by MobileNet (88.89%) and ResNet50 (84.22%), with corresponding F1-scores indicating strong class-wise performance. The system also incorporates Explainable AI (XAI) techniques to visualize model decisions, increasing interpretability and trust. A user-friendly web application was developed using HTML, CSS, JavaScript (frontend), and Flask (backend). Users can upload skin lesion images to receive instant predictions on the type of skin cancer, along with treatment guidelines, precautionary measures, and helpful resources. This project demonstrates the effectiveness of deep learning in automated skin cancer diagnosis and provides a practical tool for early screening, especially in remote or underserved areas.
To enhance the transparency of the model’s decisionmaking process, Explainable AI (XAI) techniques were integrated, enabling visualization of the regions that most influenced predictions. Finally, a fully functional web application was developed using Flask (backend) and HTML, CSS, JavaScript (frontend), allowing users to upload skin lesion images and receive instant predictions along with treatment suggestions, precautions, and useful medical resources. This system aims to assist in rapid, reliable skin cancer screening and provide a supportive diagnostic tool for healthcare professionals and individuals alike.
Key Words: Skin Cancer, Explainable AI, Deep Learning, Web Technologies
1.INTRODUCTION Skin cancer is one of the most prevalent cancers globally, with millions of new cases diagnosed each year. It primarily occurs due to the uncontrolled growth of abnormal skin cells, often triggered by excessive exposure to ultraviolet (UV) radiation from the sun or artificial sources like tanning beds. Early detection and accurate classification of skin cancer are crucial for effective treatment and significantly improve the chances of patient
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