Optimizing Melanoma Detection with Hybrid EfficientNetB3 and MobileNetV2 for Real-Time Applications

Melanoma is a biologically aggressive type of skin cancer that calls for an appropriate response and efficient treatment from the outset. The research investigates the possibilities of artificial intelligence driven systems for the detection of melanoma based on dermoscopy pictures and deep learning approaches. The proposed model uses EfficientNetB3 as a feature extractor and MobileNetV2 as a lightweight classifier, adapting transfer learning techniques to use only a small set of data. The hybrid construction offers improvements of the accuracy, precision, and screen sensitivity which make it ideal for self-diagnostic mobile applications even in low-resource settings. Although there is the problem of computational capacity and not providing real time application, AI based derivation of melanoma can aid in transforming the medical world with the ability to make prompt and accurate decisions on diagnosis. The development of transfer learning technologies, the integration of datasets, and the refinement of consideration of model for melanoma detection are regarded as promising. This new approach would enable a move toward the early detection of melanoma in patients that is affordable and efficient, leading to improved care for them.