Precision in Dermatology: Combining U-Net and Quantum Neural Networks for Melanoma Diagnosis

Melanoma is one of the most severe types of skin cancer, and early diagnosis is crucial to improving the chances of successful treatment. Deep learning models, in particular, have proven to be a highly promising application of artificial intelligence in helping dermatologists diagnose melanoma early. By using these models, dermatological images can be analyzed with greater precision, making it easier to identify suspicious lesions and differentiate between benign and malignant ones. This study shows that more accurate segmentation and classification of skin lesions can be achieved by combining models like U-Net with preprocessing methods such as Autoencoder. This can lead to better melanoma detection and treatment. Additionally, we employed a hybrid CNN-quantum neural network model for classification, which achieved an accuracy of 99.67%, a precision of 99.35%, and a recall of 99.67%.