SkinFusionNet: MobileNetV2 and vision transformer hybrid deep learning model with automated skin cancer classification

Skin cancer is one of the most widespread and potentially fatal diseases worldwide, with millions of new cases diagnosed every year. Detecting malignant skin lesions early and accurately is crucial for improving patient survival and can also help reduce the overall cost of treatment. Nevertheless, the conventional methods of diagnosing are usually time-consuming, subjective, and dependent on the skills of a clinician, thus not providing homogeneous results. In order to solve such shortcomings, this paper presents an original hybrid deep learning model, SkinFusionNet (SFN), as an automated skin lesion classifier based on dermoscopic images. The suggested SFN model is a synergetic integration of the advantages of MobileNetV2 and Vision Transformer (ViT) architectures the ability to extract local features efficiently by using MobileNetV2 and the ability to learn long-range contextual dependencies by ViT. The manipulation of both fine-grained lesion textures and larger image structures by such integration results in the high diagnostic ability of SFN at a wide range of imaging settings. The model was trained and tested on the 2018 ISIC dataset which had been augmented and balanced to equalize malignant and benign samples at 3,000 and 3,000, respectively. With SFN, outstanding performance was observed with 95.44 per cent accuracy, 95.48 per cent precision, 95.44 per cent recall and a Dice score of 0.9544, which is high compared to current CNN based and hybrid models like ResNet, DenseNet and MobileNet variants. Grad-CAM visualizations were used to mark important areas of the images that had an impact on model predictions to increase clinical trust and interpretability to offer important information about the decision-making process. The findings have shown that SFN can be an effective, interpretable and high-performance framework used in the initial detection of skin cancer. It can be easily deployed in the real world due to its lightweight nature and good ability to generalize, especially in healthcare settings that have limited resources. The given strategy is an important move on the way to making explainable AI a part of dermatological diagnostics, which would make the process of skin cancer screening faster, more credible, and more accessible.