Skin lesion classification is critical in dermatological diagnosis, where early and accurate identification of malignant lesions can significantly improve patient outcomes. Deep learning approaches, particularly transfer learning with pre-trained CNNs, have demonstrated remarkable performance in automated dermoscopic image analysis. However, the optimal configuration of transfer learning components---including backbone architecture, fine-tuning strategy, and data augmentation intensity---remains an open question. In this paper, we present a systematic comparative study on the HAM10000 dataset, evaluating three CNN architectures (ResNet50, DenseNet121, EfficientNet-B0), three fine-tuning stra... More >

This article [1] has been retracted at the request of the authors. After publication, the authors conducted a further verification of the experimental code and results. Regrettably, an error was discovered in the implementation of the adaptive encoding template optimization algorithm (Section~3.6), as a consequence of which the experimental results reported in the manuscript cannot be reproduced. As the reported results cannot be reproduced, the validity of the paper's main conclusions cannot be substantiated. The authors determined that retraction is the most responsible course of action in order to prevent misleading future research. All authors were contacted regarding this retraction. Di... More >

Hyperspectral image classification is a central task in remote sensing information extraction. Conventional approaches follow a reconstruct-then-classify paradigm, which entails large data volumes, high computational cost, and poor real-time performance. This paper presents an adaptive hyperspectral direct classification method based on computational spectral imaging. A Digital Micromirror Device (DMD) is used to spectrally encode and modulate the incident light, enabling direct output of two-dimensional spatial classification results without reconstructing the three-dimensional spectral data cube. First, a classification-oriented encoding template is designed via Fisher discriminant analys... More >