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 >

Early recognition of maize leaf disorders and applying precautionary measures on time may help to increase the yield and quality. This study introduces an architecture for the recognition and categorization of maize leaf diseases based on the deep Inception-v3 and maximum value-based color features. The core steps of the designed framework include data acquisition, feature extraction, fusion, and classification. The maize leaf image dataset is utilized, which is publicly available on Kaggle, comprising four classes. The deep learning features are collected by applying the transfer learning approach to the pre-trained Inception-v3 model. In addition to the deep features, maximum value-based c... More >

Breast cancer is a leading cause of cancer-related mortality worldwide, making accurate histopathological subtype discrimination critical for timely clinical intervention. Existing deep learning approaches often evaluate limited settings (binary or multi-class, single magnification), restricting comparative utility and clinical interpretability. This study proposes a unified Cross Stage Partial Network (CSPNet)-based framework for comprehensive classification on the BreaKHis dataset. A CSPResNet50 backbone pre-trained on ImageNet was extended with a multi-scale Feature Pyramid-style aggregation head, Squeeze-and-Excitation channel attention, dual Global Average and Max Pooling per scale (153... More >