Optimizing Skin Cancer Detection Using the MobileNet Deep Learning Model: A Lightweight and Efficient Approach

Early and precise diagnosis of skin cancer is essential for successful treatment and enhanced patient survival. Automated skin cancer classification, however, is still a challenging task because of the variability in lesion appearance and the requirement for high diagnostic accuracy. In this work, we investigate the possibility of using deep learning to improve skin cancer detection. We apply the MobileNet model, a light and fast convolutional neural network (CNN), to classify skin lesions based on the HAM10000 dataset, which contains 10,000 labeled dermoscopic images. MobileNet is designed for low-resource settings and is therefore ideal for real-time medical applications, such as mobile and edge devices. Our experimental results show that MobileNet has high classification accuracy 92.86% and validation accuracy 98.56%, while greatly lowering computational costs and inference time relative to larger deep-learning models. This research brings out the effectiveness of MobileNet in detecting skin cancer as a feasible scalable and cheap diagnosis device. Increased effectiveness in machine detection results in overall advancement of AI-driven healthcare, promoting early dermatologist diagnosis and better patient care.