Optimized EfficientNetB5 Framework for Skin Cancer Classification

The complex assortment of disorders in the human body known as cancer presents numerous difficulties for modern-day medicine. Its molecular mechanisms are so well understood for research advancements in many sectors. Immunotherapies and targeted therapeutics are quite potential for individualized treatments. To enhance early-stage detection and more effective treatments, research in healthcare must continue. In order to evaluate medical imaging data and precisely identify minor patterns indicative of nine types of skin cancer, this research proposes a conceptual framework for diagnosing many skin cancer lesions by utilizing the capabilities of CNN (i.e., Convolutional Neural Networks).Through the usage of the Adam optimizer, the EfficientNetB5 network designs and integrates various adaptive learning rates, allowing it to dynamically adapt and maximizes performance during training. The proposed model’s multi-layered architecture allows it to recognize minute characteristics at microscopic and macroscopic levels, ensuring a comprehension of possible existing cancers. For three hidden layers and output channels of 32, 54, and 128 respectively, the proposed model has the potential to greatly increase diagnosis accuracy and enable rapid treatments. With accuracy of 92.68% for 9 different forms of skin cancer in the dataset, the subsequent modelling assurances class balancing and augmentation using XGBoost classifiers, as it will train the images with better feature handling.