Interpretable Melanoma Detection Utilizing Stacked Modeling Approach

A skin lesion is a region of the skin that has grown differently from the rest of the skin. A smaller version of the ISIC 2018 lesion dataset only has the two classifications of malignant and benign. The larger dataset has seven classes. While benign tumors are not carcinogenic, malignant tumors are. Malignant tumors are more capable of spreading throughout the body quickly and multiplying. For a patient to survive, it is crucial to find malignant skin lesions as soon as possible. Machine learning and deep learning models have become essential tools for spotting skin lesions. However, issues like picture occlusions and unbalanced datasets have made it difficult to achieve high accuracy. The interpretable technique for non-invasive cancer skin cancer detection presented in this research uses ensemble stack of algorithms for learning models and deep learning. The balanced photos of both kinds of facial moles make up the dataset employed to train the predictive models. Through crossover validation on the training set, predictions from these basic models aid in level one model training. Models using deep learning that have already been trained on ImageNet data are used for transfer learning. Each model’s performance is evaluated on an individual basis. The models based on deep learning are then put together in different ensembles and assessed. The research uses shapely adaptive explanations to generate heatmaps that emphasize the areas of the images that are most suggestive of the condition, improving interpretability. This strategy makes it easier for dermatologists to comprehend the model’s findings in a therapeutically useful way. curves are examples of evaluation measures. The study reveals the skin lesion categorization model that performs the best.