Integrating Deep Learning and Machine Learning Approaches for Improved Accuracy in Skin Disease Detection

This work suggests a new model for identifying skin diseases by combining deep learning and machine learning models in a complementary way. When these methods are used in tandem, they improve the precision and effectiveness of diagnosing a variety of skin disorders. CNNs (Convolutional Neural Networks), in particular, are used in DL(deep learning) models for feature extraction because of their ability to recognize and extract relevant characteristics from complex skin images automatically. To classify the skin conditions, these features are then passed through several kinds of machine learning classification models, such as Random Forests, SVM (Support Vector Machines), and KNN (k-nearest Neighbors). The combination of deep learning and conventional machine learning techniques provides a solid framework that draws on the benefits of each methodology. Machine learning classification algorithms offer flexibility and clarity in the decision-making process, whereas deep learning models are useful in identifying complex patterns in image data. This combination increases the system’s capacity to generalize across various skin disease types and improves diagnostic accuracy. The experimental results, which demonstrate noticeable improvements in important performance metrics like accuracy, precision, and F1-score, highlight the effectiveness of the proposed strategy. The system performs better at distinguishing between related skin conditions that are frequently difficult to diagnose by implementing this hybrid approach. This study includes increasing research on AI-driven healthcare solutions, giving dermatologists and other medical professionals a more efficient and reliable tool for diagnosing skin diseases. This study ultimately shows how deep learning and machine learning models can be combined to produce more advanced and precise medical imaging diagnostic systems. Our method demonstrated its potential for real-world implementation with a noteworthy accuracy of 0.97 using the MSCV(Mobilenet Svm Cross Validation) model.