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This study presents a lightweight pipeline for skin lesion detection,addressing the challenges posed by imbalanced class distribution and subtle oratypical appearances of some lesions. The pipeline is built around alightweight model that leverages ghosted features and the DFC attentionmechanism to reduce computational complexity while maintaining highperformance. The model was trained on the HAM10000 dataset, which includesvarious types of skin lesions. To address the class imbalance in the dataset,the synthetic minority over-sampling technique and various image augmentationtechniques were used. The model also incorporates a knowledge-based lossweighting technique, which assigns different weights to the loss function atthe class level and the instance level, helping the model focus on minorityclasses and challenging samples. This technique involves assigning differentweights to the loss function on two levels - the class level and the instancelevel. By applying appropriate loss weights, the model pays more attention tothe minority classes and challenging samples, thus improving its ability tocorrectly detect and classify different skin lesions. The model achieved anaccuracy of 92.4%, a precision of 84.2%, a recall of 86.9%, a f1-score of 85.4%with particularly strong performance in identifying Benign Keratosis-likelesions (BKL) and Nevus (NV). Despite its superior performance, the model'scomputational cost is considerably lower than some models with less accuracy,making it an optimal solution for real-world applications where both accuracyand efficiency are essential.

Attention-Driven Lightweight Model for Pigmented Skin Lesion Detection