An improved approach for early diagnosis of Parkinson’s disease using advanced DL models and image alignment

Abstract

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.

KEYWORDS:

• Parkinson's disease prediction
• U-Net-based segmentation
• DenseNet architecture
• and GUI-based classification

Acknowledgement

There is no acknowledgement involved in this work.

Authorship contributions

All authors are contributed equally to this work.

Data availability statement

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

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No potential conflict of interest was reported by the author(s).

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No participation of humans takes place in this implementation process.

Human and animal rights

No violation of Human and Animal Rights is involved.

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Additional information

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