Skin Cancer Prediction using Modified EfficientNet-B3 with Deep Transfer Learning

Skin cancer is considered to be a very perilous kind of cancer and is recognized as a significant contributor to global mortality rates. The timely identification of skin cancer offers a chance to reduce the cumulative rate of death. The primary method of diagnosing skin cancer predominantly relies on visual inspection, a technique that is known to possess limitations in terms of accuracy. There have been proposals to employ deep-learning algorithms to assist physicians in promptly and accurately detecting skin cancers. This paper presents skin cancer perdition with Deep Transfer Learning (DTL). The DTL model utilized in the proposed model is EfficientNet-B3. The dataset utilized in this study was obtained from the Kaggle skin cancer dataset. Prior to being applied to the updated EfficientNet-B3, the data undergo preprocessing techniques such as rescaling and random adjustments to brightness and/or contrast, with a range of ±20%. Prior to putting it to the DTL model, the data undergoes pre-processing and augmentation. For training 80% and for testing 20% of data is used. The proposed model's training accuracy is around 98.64%, and its validation accuracy is approximately 90.6%.