Ensemble Model with Deep Learning for Melanoma Classification

The skin is the utmost sensitive portion of the human body and needs to be constantly cared for and shielded from other potentially harmful elements, including UV radiation, light, heat, and dust. Skin carcinoma is a fatal condition that influences people. Melanocytes, which control the pigmentation of human skin and are the source of the skin cancer identified as melanoma, begin to grow there. Proper diagnosis and identification of skin malignancies like melanoma are crucial to lowering the skin malignancy fatality rate. Acrallentiginous melanoma, a type of illness that also contains benign neoplasms, is classified in this study. The proposed stacking ensemble strategy to classify melanoma uses a variety of pre-trained techniques, such as Inceptionv3, MobileNetv2, and others, and is based on the idea of machine learning coupled with fine-tuning. The pre-trained deep learning architectures that will be utilized for transfer learning are selected using models. A unique stacking ensemble-based methodology is offered to support generalizability and robustness by integrating precisely calibrated, pre-trained deep learning with logistic regression methods for melanoma classification. To test the effectiveness of the proposed technique, a Harvard dataset is employed. In order to evaluate the outcomes of various augmentation procedures, numerous tests have been conducted. The results validate the claim that the proposed method achieves a 99.2% accuracy rate and outperforms state-of-the-art methodologies.