YOLOv11 with spatial attention and preprocessing enhancements for accurate skin cancer classification - Kareem, Aythem Khairi; Nafea, Ahmed Adil; Aminuddin, Afrig (Al-Nahrain Journal for Engineering Sciences, 2026)
This work suggests a Deep Learning (DL) architecture based on You Only Look Once YOLOv11 for Skin Cancer (SC) detection. The similarity between malignant and benign lesions makes visual inspection a failure to distinguish between them. To solve this problem, the proposed approach uses a 3-step pre-processing stage, namely hair removal, color normalization, and Contrast Limited Adaptive Histogram Equalization (CLAHE) contrasts, has been conducted to eliminate artifacts and improve image quality. Balanced data augmentation on the training set of the PROVe-AI dataset. In this process, YOLOv11 with C3k2 module and C2PSA module showed significant results in optimized multi-scale feature collection and spatial interest. The experimental outcome demonstrates that the proposed model has a classification accuracy of 93.09% and led the baseline models, such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Artificial Neural Network (ANN). The proposed optimized YOLOv11 architecture allows for skin cancer detection in a computationally efficient framework with promising preliminary results so that the proposed approach can be a beneficial Artificial intelligence (AI) tool for early diagnosis, particularly in a lack of high-tech medical facilities.