CNN-SSA: Enhanced Skin Cancer Diagnosis from Dermoscopic Images Using an Optimized Convolutional Neural Network and Salp Swarm Optimization

CNN-SSA, a skin cancer diagnosis system that classifies dermoscopic images using a convolutional neural network tuned by salp swarm optimisation.

CNN-SSA, a skin cancer diagnosis system that classifies dermoscopic images using a convolutional neural network tuned by salp swarm optimisation. This reference project walks through the whole job, from the problem and the design to the build and the evaluation, so that a developer can study how it works, run it and extend it.

It suits learners who want to work with neural networks end to end: preparing datasets, designing and training a model, tuning it, evaluating it properly and explaining the outcome.

The main parts are dataset preparation and augmentation, model design and training, evaluation with standard metrics, plus error analysis, and a simple interface or API to try the model. The reference build uses Python, PyTorch, TensorFlow, and NumPy, with a clear project structure, setup instructions and sample data so that it can be run and extended.