Integration of Multimodal Data Sources for Enhanced Skin Disease Classification and Cancer Prediction: A Study Leveraging Pre-Trained Models on HAM_10000 Metadata and Squamous Cell Carcinoma (SCC) Images.

Abstract

Skin diseases, disorders, and deficiencies are fundamental issues in the human body. Over the last few decades, they have become widely prevalent, often leading to cancer. This spectrum of ailments ranges from benign conditions to potentially life-threatening cancers, posing significant challenges to global health. Accurate classification of these diseases and timely prediction of malignant transformations are crucial for effective diagnosis and treatment planning. In our research paper, we propose an innovative approach that levitation multimodal data integration to enhance the precision of skin disease classification and improve the prediction of cancerous transformations. The study utilizes the HAM_10000 Metadata, a dataset comprising diverse skin lesions, along with 100 high-resolution images of Squamous Cell Carcinoma (SCC). Pre-trained models, specifically multi-model CNNs, are employed to extract intricate patterns and features. By amalgamating information from these heterogeneous sources and implementing advanced machine learning techniques, the results highlight the effectiveness of this integrative methodology. This approach can revolutionize the fields of dermatology and oncology by providing patients with skin diseases accurate, efficient, and early diagnoses of skin disease. Through this method, it was discovered that SCC images might hold a higher priority for skin cancer identification, achieving an accuracy rate of 92% on the original dataset.