Personalized recommendation system to handle skin cancer at early stage based on hybrid model

Skin cancer is one of the most prevalent and harmful forms of cancer, with early detection being crucial for successful treatment outcomes. However, current skin cancer detection methods often suffer from limitations such as reliance on manual inspection by clinicians, inconsistency in diagnostic accuracy, and a lack of personalized recommendations based on patient-specific data. In our work, we presented a Personalized Recommendation System to handle Skin Cancer at an early stage based on Hybrid Model (PRSSCHM). Preprocessing, improved deep joint segmentation, feature extraction, and classification are the major steps to identify the stages of skin cancer. The input image is first preprocessed using the Gaussian filtering method. Improved deep joint segmentation is employed to segment the preprocessed image. A set of features including Median Binary Pattern (MBP), Gray Level Co-occurrence Matrix (GLCM), and Improved Local Direction Texture Pattern (ILDTP) are extracted in the next step. Finally, the hybrid classification includes Improved Bi-directional Long Short-Term Memory (Bi-LSTM) and Deep Belief Network (DBN) used for the classification process, where the training will be carried out by the Integrated Bald Eagle and Average and Subtraction Optimizer (IBEASO) algorithm via optimizing the weights of the models.