Deep Learning-Based Approach for Skin Cancer Detection

Melanoma skin cancer is one of the fastest growing skin cancer types worldwide and remains one of the most challenging public health issues to manage. Mortality rates can be decreased with timely and precise treatment, and so the early-stage identification of skin cancer is still a vital need. In this paper, we develop a skin lesion classification system that utilizes deep learning techniques on dermoscopic images. We built a custom convolutional neural network (CNN) with TensorFlow and Keras that performs binary classification on malignant and benign skin lesions. Each dermoscopic image is subjected to resizing to a resolution of 224 x 224 pixels and subsequent steps of preprocessing, which include normalization as well as data augmentation, to improve model generalization. The proposed CNN architecture consists of three “convolutional pooling” blocks followed by fully connected dense layers, thus achieving high accuracy and equally high-performance metrics on a balanced dataset. The model produced robust evaluation results—with the accuracy, precision, and recall alongside F1-score and AUC-ROCs marking the metrics of strength—confirming these claims. The deep learning methods, as discussed here, can potentially support medical decision-making in dermatology at glance, and this potential continuously needs further research. After that, plans include working on transfer learning, multimodal inputs, and moving the system to mobile devices for real-time diagnosis.