Optimizing Deep Learning Algorithms for Predictive Modeling of Melanoma

Melanoma is a dangerous form of skin cancer if not detected and treated early. Deep learning algorithms have been utilized to make predictions in the diagnosis of melanoma, which is well and good, however, there remains much room for optimization that will result in more accurate and efficient deep-learning-based models. This project aims to provide a way for deep learning algorithms to suit best the task of predicting melanoma. This will entail looking at different neural network architectures and tweaking hyperparameters to get the optimal results in terms of accuracy and generalization. Moreover, we will apply data augmentation and transfer learning techniques to enhance the training of our model and diversify the training dataset. Potential biases in the data will be identified and corrected to improve algorithm performance. This could involve classes that are imbalanced or biased toward skin tones. It will also focus on increasing the interpretability and explainability of the algorithms to build trust in these predictions. These optimized deep learning algorithms can now be used in practice such as mobile apps or telemedicine- for doctors' and patients' early melanoma recognition. In summary, this project works to create a deep learning algorithm that is more accurate and quicker than traditional approaches for predicting melanoma detection with the end goal of earlier diagnoses that may lead to saving lives.