Introduction The automated classification of dermoscopic skin lesions is inherently challenging due to pronounced class imbalance, minimal inter-class variance, visual similarity across lesion types, and the requirement for clinically interpretable predictive outcomes. Methods The present study designed a heterogeneous...
N. Sravani, Srinivas Koppu· Frontiers in Medicine· 0 citations
Introduction Kidney abnormalities, including cysts, tumors, and stones, are the most common renal disorders that can lead to severe complications such as chronic kidney disease or renal failure. Deep learning-based medical image analysis offers an effective approach for the accurate classification of kidney abnormaliti...
Sai Sri Hemantha Konala, Srinivas Koppu· Frontiers in Artificial Inte...· 0 citations
Introduction Skin cancer is among the most prevalent and life-threatening malignancies worldwide. Early and accurate detection significantly improves therapeutic outcomes. Automated classification of dermoscopic skin lesions remains challenging due to class imbalance, inter-class visual similarity, and lack of interpre...
NE. Sravani, Srinivas Koppu· Frontiers in Public Health· 0 citations
A novel two-stage deep learning architecture that integrates self-supervised representation learning with supervised classification for kidney CT image analysis using a publicly available kidney CT image dataset is introduced.
Sai Sri Hemantha Konala, Srinivas Koppu· Frontiers in Medicine· 0 citations
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