Efficient Net Approach for Skin Cancer Detection for Early Intervention

Skin cancer is a common disease that can be fatal, and in order to effectively treat it, early discovery is essential. It is essential to treat skin cancer as soon as possible to stop its spread and any potentially fatal effects. Deep learning methods have recently demonstrated potential to enhance medical image processing, especially in the dermatology domain. This work employs the EfficientNet approach to automatically identify skin cancer from dermatoscopic pictures. EfficientNet algorithm have the potential to increase diagnosis speed and accuracy, resulting in early detection and treatment. This design is very resource-efficient and performs well because of the adoption of a compound scaling technique, which optimizes the exact depth, exact width, and resolution about the model. The objective of the study is to determine how to enhance skin cancer categorization and detection using EfficientNet. The proposed method utilises a dataset containing a range of dermatoscopic images containing both benign and malignant skin diseases. To ensure a realistic distribution of examples, the dataset has been carefully chosen, which enhances the model’s capacity to generalise to actual scenarios. The methodology consists of preprocessing the dermatoscopic pictures, optimizing the EfficientNet model that has been trained on the skin cancer dataset, and expanding the training dataset using data augmentation methods. Sensitivity, specificity, and overall accuracy are used to assess the trained model’s performance using an updated test set.