MULTI-LEVEL FEATURE EXTRACTION AND SELECTION WITH STRONG CORRELATION AND EFFECTIVE CLUSTERING MODEL FOR ACCURATE MELANOMA DETECTION

Melanoma, a fatal and aggressive form of skin cancer has continuously increased in incidence rates across the globe, emphasizing the necessity for early precise detection modalities to achieve better 5 year survival outcomes. This research presents a significant contribution to the field of Information Technology by proposing an advanced automated framework for accurate melanoma detection through a combination of multi-level feature extraction, strong correlation analysis, and intelligent clustering. With the ever-increasing volume of medical imaging data and the limitations of manual diagnosis, IT-based solutions such as machine learning and image processing are not just supportive tools they are transformative technologies driving modern healthcare innovation. This work underlines the indispensable role of IT in developing intelligent, data-driven healthcare systems capable of early diagnosis, ultimately contributing to increased survival rates and reduced healthcare burdens. This research proposes a Multi Level Feature Extraction and Rank Linked Feature Set with Clustering (MLFE-RLFSC) Model for accurate melanoma detection. Furthermore, this intelligent feature selection process helps in reducing the risk of over fitting and also improves significantly generalization performance of model to classify melanoma more accurately. In addition, our method integrates a clustering model which groups the type identical features so overall redundancy can be diminished as well that in turn enhance the general security level of detection systems. The proposed model achieved 98.8% accuracy in Multi Level Feature Extraction, 99.2% accuracy in Feature Rank Allocation, 98.6% accuracy in Clustering, 99.3% accuracy in Rank Linked Feature Set Generation and 99.2% accuracy in Classification of benign and malignant tumours. The proposed model when compared to the traditional methods performs better in clustering and melanoma detection. The proposed model with minimum false predictions accurately detects the melanoma for providing early diagnosis.