Publication

Abstract : Accurate skin mole categorization is essential for early skin cancer identification and treatment in dermatological diagnostics. This study uses the sophisticated transfer learning approach using the EfficientNet V2 B0 model, a convolutional neural network renowned for its effectiveness and high accuracy in image classification tasks, to provide a novel method of differentiating benign from malignant skin moles. The work’s primary contribution is optimizing the EfficientNet V2 B0 model for high accuracy in a medical setting by tailoring it to the unique characteristics of dermatological images. By incorporating unique layers and using specific training methodologies, we improved the transfer learning model’s capacity to identify minute characteristics that may indicate skin mole malignancy. After a thorough validation, the model showed an incredible 98.7% classification accuracy, outperforming the industry norms. This high degree of accuracy opens the door for more dependable, non-invasive skin cancer screening techniques as well as highlights the possibilities of using cutting-edge deep learning models in medical diagnostics. The results of this study might lessen the need for invasive biopsy procedures and provide prompt therapy, which would impact early identification and intervention tactics.