Izvorni znanstveni članak
https://doi.org/10.1080/00051144.2023.2284030
An improved approach for early diagnosis of Parkinson’s disease using advanced DL models and image alignment
S. Kanagaraj
; Department of Information Technology, Kumaraguru College of Technology, Coimbatore, India
*
M.S. Hema
; Department of Information Technology, Anurag University Medchal, Hyderabad, India
M. Nageswara Guptha
; Computer Science and Engineering, Sri Venkateshwara College of Engineering, Bengaluru, India
* Dopisni autor.
Sažetak
An innovative approach to enhance image alignment through affine transformation, allowing
images to be rotated from 0 to 135 degrees. This transformation is a crucial step in improving the diagnostic process, as image misalignment can lead to inaccurate results. The accurate
alignment sets the stage for a robust U-Net model, which excels in image segmentation. Precise
segmentation is vital for isolating affected brain regions, aiding in the identification of PD-related
anomalies. Finally, we introduce the DenseNet architecture model for disease classification, distinguishing between PD and non-PD cases. The combination of these DL models outperforms
existing diagnostic approaches in terms of acceptance precision (99.45%), accuracy (99.95%),
sensitivity (99.67%), and F1-score (99.84%). In addition, we have developed user-friendly graphical interface software that enables efficient and reasonably accurate class detection via Magnetic Resonance Imaging (MRI). This software exhibits superior efficiency contrasted to current
cutting-edges technique, presenting an encouraging opportunity for early disease detection. In
summary, our research tackles the problem of low accuracy in existing PD diagnostic models
and addresses the critical need for more precise and timely PD diagnoses. By enhancing image
alignment and employing advanced DL models, we have achieved substantial improvements in
diagnostic accuracy and provided a valuable tool for early PD detection.
Ključne riječi
Parkinson’s disease prediction; U-Net-based segmentation; DenseNet architecture; and GUI-based classification
Hrčak ID:
326205
URI
Datum izdavanja:
11.3.2024.
Posjeta: 0 *