A Novel Approach to Derma Quantum (Skin Disease Detection and Classification Using ResNet50 and QCNN)

Multiclass disease classification is a crucial task in medical diagnostics, but achieving high accuracy across diverse disease categories remains challenging. This paper presents a novel approach that integrates Quantum Convolutional Neural Networks (QCNN) with the ResNet architecture to enhance multiclass skin disease classification. The model utilizes quantum computing principles to improve feature extraction and classification performance. We evaluate the model on a dataset comprising 16 skin disease classes from the DermNet dataset, covering both common and rare conditions like acne, melanoma, Eczema, Seborrheic Keratoses, Tinea Ringworm, Bullous disease, Poison Ivy, Psoriasis, Vascular Tumors, etc. Experimental results demonstrate that our QCNN-ResNet model achieves a classification accuracy of 86%, significantly surpassing the 66% accuracy of traditional ResNet models. This improvement is attributed to the enhanced feature representation provided by quantum layers, which capture complex patterns in medical images more effectively. Furthermore, the model exhibits robustness to noisy data, maintaining high accuracy across varying image quality levels. These findings highlight the potential of quantumenhanced neural networks in advancing medical diagnostics. Future research will explore scaling the model to larger datasets and extending its application to other medical imaging domains.