Improved Skin Cancer Detection Using Efficient NetB7 Machine Learning

Early identification may help prevent or cure skin cancer, a serious worldwide health problem. To increase the accuracy and effectiveness of skin cancer prediction, this research employs machine learning approaches. The HAM10000 dataset was used to train and test our brand-new skin cancer prediction model, which is based on the state-of-the-art EfficientNetB7 architecture. To resolve class imbalance concerns that are prevalent in dermatological datasets, data augmentation procedures are employed to give equitable representation in the training data. The RGB attributes that were taken from the photographs and incorporated into our model. Our approach outperforms conventional machine learning models with an accuracy of 89%, which is promising. In addition to improving skin cancer prediction algorithms, this research provides a fast and affordable option to aid in early detection. Our research demonstrates the value of accurate skin cancer detection in improving patient outcomes and reducing healthcare expenditures, as well as the potential of machine learning in dermatological diagnostics. This research not only paves the way for future developments in the area of automated skin cancer diagnosis but also offers hope for wider applications in the area of medical image analysis.