<![CDATA[This study investigates the application of incremental learning techniques to enhance the classification of skin diseases in dermoscopic images. The research aims to develop a model capable of continuous adaptation to new data while retaining previously acquired knowledge. Two datasets were utilized: acne images and the HAM10000 dataset comprising various skin lesions. The methodology involved initially training a ResNet-18 convolutional neural network on 1,052 samples across eight classes, followed by an incremental learning phase incorporating 800 additional data points. Rigorous preprocessing steps were implemented to ensure data quality, including cropping, resizing, and normalization. Results demonstrate that the base model achieved 87% accuracy on the test set, which improved to 90% after the incremental learning process. Detailed analysis revealed significant improvements in precision, recall, and F1-scores for several skin disease classes, notably for challenging categories such as Basal Cell Carcinoma (bcc) and Dermatofibroma (df). Confusion matrix analysis and Grad-CAM visualizations provided insights into the model's decision-making process and its focus on clinically relevant features. The study also implemented a Streamlit application to demonstrate real-time classification capabilities and the system's adaptability in a simulated clinical environment. These findings have potential clinical applications, particularly in teledermatology systems where adaptive algorithms can accommodate new dermatological data over time. The study highlights the potential of incremental learning in creating accurate, adaptable, and clinically relevant AI models for skin disease classification in evolving medical practices.]]>
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