54 citations · 56 across the 4 of their papers we have counts for
4 papers
From Pixels to Explanations: Interpretable Diabetic Retinopathy Grading with CNN-Transformer Ensembles, Visual Explainability and Vision-Language Models
Pir Bakhsh Khokhar, Carmine Gravino, Fabio Palomba +2
The quality of diabetic retinopathy (DR) screening relies on the ability to correctly grade severity; however, many deep-learning (DL) classifiers cannot be easily interpreted in t…
Transformer-Based Multi-Modal Temporal Embeddings for Explainable Metabolic Phenotyping in Type 1 Diabetes
Pir Bakhsh Khokhar, Carmine Gravino, Fabio Palomba +2
Type 1 diabetes (T1D) is a highly metabolically heterogeneous disease that cannot be adequately characterized by conventional biomarkers such as glycated hemoglobin (HbA1c). This s…
Towards Transparent and Accurate Diabetes Prediction Using Machine Learning and Explainable Artificial Intelligence
Pir Bakhsh Khokhar, Viviana Pentangelo, Fabio Palomba +1
Diabetes mellitus (DM) is a global health issue of significance that must be diagnosed as early as possible and managed well. This study presents a framework for diabetes predictio…
Advances in Artificial Intelligence forDiabetes Prediction: Insights from a Systematic Literature Review
Pir Bakhsh Khokhar, Carmine Gravino, Fabio Palomba
This systematic review explores the use of machine learning (ML) in predicting diabetes, focusing on datasets, algorithms, training methods, and evaluation metrics. It examines dat…