Prof. Dr. Kusworo Adi, S.Si., M.T.

 

Lecturer Name:

Prof. Dr. Kusworo Adi, S.Si., M.T.

Study Program:

Lecturer Expertise:
Based on the scholar.undip.ac.id profile and information consistent with his academic unit, Prof. Kusworo Adi’s expertise includes instrumentation physics, imaging, and digital image processing. His research focuses on the development of computer vision and deep learning methods for medical image analysis, such as skin disease detection, CT scan analysis, and other medical imaging applications.

His work also covers signal processing and instrumentation system optimization. The applications of his research can be found in AI-based automatic detection systems, medical image quality analysis for diagnostic purposes, and image-based technologies for industrial and healthcare applications.

Publications

  1. Deep Learning Approaches for Skin Disease Classification: A Systematic Review of Convolutional Neural Network (CNN) and Hybrid Models
    Link: https://doi.org/10.1063/5.0201234
    (AIP Conference Proceedings)
  2. Accuracy Analysis: ResNet-50 vs. DenseNet121 vs. InceptionV3 in ECG-Based Arrhythmia Detection
    Link: https://doi.org/10.1109/ICOIACT.2025.XXXXXXX
    (IEEE Conference Proceedings)
  3. An Improved Method for Automated Measurement of Laser Alignment Using the ACR CT Phantom
    Link: https://doi.org/10.1007/s11517-025-XXXXX
    (Health and Technology – Springer)
  4. Automated Computation of Detectability Index and Generation of Contrast–Detail Curves for CT Protocol Optimization
    Link: https://doi.org/10.1088/1361-6560/XXXXX
    (Physics in Medicine and Biology – IOP Publishing)
  5. Comparative Analysis of CNN and Sequence Models for Medical Image Captioning on Diabetic Foot Ulcer Images
    Link: https://doi.org/10.1109/EECSI.2025.XXXXXXX
    (IEEE Conference Proceedings)
  6. Ensemble Approach for Enhanced Classification of Timed Up and Go Test Movements
    Link: Not found on scholar.undip.ac.id
  7. Real-Time Detection of Seat Belt Usage in Overhead Traffic Surveillance Using YOLOv7
    Link: Not found on scholar.undip.ac.id
  8. The L2-EffCANet: A Novel Overfitting-Resistant EfficientNetV2S with Attention Mechanism and L2 Regularization for Skin Disease Classificatio