Skin Cancer Detection Using Deep Learning Techniques: A Review

Melanoma and other skin cancers offer serious health hazards, especially given the high fatality rates linked with late detection. Early identification is critical for improving patient outcomes; yet, present clinical approaches are often subjective and difficult, resulting in varied accuracy. This research investigates the application of deep learning (DL) for skin cancer diagnosis, with a focus on four convolutional neural network (CNN) architectures: AlexNet, VGG16, ResNet, and DenseNet. These models are tested for their ability to categorise skin lesions and differentiate between melanoma and non-melanoma instances using extensive image analysis. These designs provide a strong alternative to standard diagnostic approaches by harnessing CNNs' skills in feature extraction and pattern recognition. The research emphasises a few major findings: ResNet and DenseNet surpass AlexNet and VGG16 in terms of classification accuracy and generalisability on a variety of datasets. However, substantial impediments continue to exist, including data imbalance, poor interpretability of model conclusions, and the difficulty of generalising across distinct patient populations. Addressing these constraints is critical to increasing the clinical value of CNN-based systems. Future study should concentrate on incorporating multimodal data, such as clinical information and imaging data, to improve model robustness. Furthermore, establishing explainable AI (XAI) frameworks will be critical to improving confidence in AI-powered diagnostic tools. Despite these obstacles, deep learning provides a scalable and cost-effective method for early skin cancer diagnosis, which has the potential to drastically cut death rates and enhance treatment results.