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Fusing CNN Models for Improved Parkinson's Disease Detection from Handwritten Features
  • Sabrina Benredjem,
  • Mekhaznia tahar
Sabrina Benredjem
Universite Echahid Cheikh Larbi Tebessi

Corresponding Author:[email protected]

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Mekhaznia tahar
Universite Echahid Cheikh Larbi Tebessi
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Abstract

Neurodegenerative diseases (NGD) are a group of progressive neurological disorders, like as Alzheimer’s disease (AD) and Parkinson’s disease (PD) that result in the progressive loss of neuronal structure or function. This can lead to a variety of symptoms, including cognitive decline, movement disorders, and dementia. In this study, we interest in PD, one of the neurological diseases that causes the loss of dopamine-producing neurons in the brain, leading to movement disorders. The early diagnosis of PD seems the best way to improve the quality of the patient’s life by prescribing the appropriate treatment. The relevant observed symptoms are often discreet; these include slow movement, decreased performance in carrying out daily tasks, tremors, muscle stiffness, and various other psychological symptoms. Handwriting or drawing analysis is one of the dominant mechanisms supporting the early diagnosis and assessment of PD. Based on that, to improve the reliability of Parkinson’s disease (PD) detection, we implemented various data augmentation techniques to increase the size of the dataset. We then deploy and train various architectures of deep convolutional neural networks (CNNs), each capturing different salient features and aspects of the input data due to their unique layout and structure. We then carefully select promising feature vectors and apply various early fusion strategies before the final classification step. Early fusion combines the feature vectors extracted by multiple CNNs at an early stage, allowing the classification model to learn and recognize from different representations of the data provided by these CNNs. This technique is very beneficial as it improves the model’s ability to capture a wide range of features and significantly improves overall system performance. Our experimental results demonstrate that the fusion of frozen features from multiple deep CNN models yields a substantial improvement in accuracy, achieving an impressive exactness rate of 96.29%. This performance surpasses that of individual CNN models and even outperforms other state-of-the-art approaches, highlighting the effectiveness of our fusion-based strategy in enhancing PD detection accuracy.