Attention-Driven Lightweight Model for Pigmented Skin Lesion Detection
Mingzhe Hu Xiaofeng Yang
This study presents a lightweight pipeline for skin lesion detection,
addressing the challenges posed by imbalanced class distribution and subtle or
atypical appearances of some lesions. The pipeline is built around a
lightweight model that leverages ghosted features and the DFC attention
mechanism to reduce computational complexity while maintaining high
performance. The model was trained on the HAM10000 dataset, which includes
various types of skin lesions. To address the class imbalance in the dataset,
the synthetic minority over-sampling technique and various image augmentation
techniques were used. The model also incorporates a knowledge-based loss
weighting technique, which assigns different weights to the loss function at
the class level and the instance level, helping the model focus on minority
classes and challenging samples. This technique involves assigning different
weights to the loss function on two levels - the class level and the instance
level. By applying appropriate loss weights, the model pays more attention to
the minority classes and challenging samples, thus improving its ability to
correctly detect and classify different skin lesions. The model achieved an
accuracy 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-like
lesions (BKL) and Nevus (NV). Despite its superior performance, the model's
computational cost is considerably lower than some models with less accuracy,
making it an optimal solution for real-world applications where both accuracy
and efficiency are essential.