InvNets: A Novel Approach for Parkinson Disease Detection Using Involution Neural Networks

Résumé fourni par la source

Parkinson's disease (PD) is a progressive neurological ailment that requires early discovery for effective treatment. Traditional PD detection methods use convolutional neural networks (CNNs), which can be computationally intensive due to their huge number of parameters. This study proposes a novel approach for PD classification utilizing Involutional Neural Networks (InvNets), which reduce the parameter-intensive nature of CNNs. The involution kernel differs from the convolution kernel in that it is both location-specific and channel-agnostic. This location-specific operation enhances the network's ability to acquire detailed elements in medical images by adapting to diverse visual patterns across spatial regions. In that regard, magnetic resonance imaging (MRI) has become an important modality for PD detection. Furthermore, image preprocessing, data augmentation techniques, and the use of Block-Matching and 4D Filtering (BM4D) for noise filtering improve the robustness and accuracy of our proposed InvNets models. The proposed model results showed that InvNets outperformed traditional CNNs in classification tasks. The InvNets architecture has shown outstanding performance, achieving a training accuracy of 99.8%, validation accuracy of 98.5%, testing accuracy of 99.1%, precision 99%, recall 99%, and F1-score 99%. The suggested InvNets model is extremely effective for medical image processing, especially in situations with limited computer resources, as seen by improved accuracy and a lower parameter count. As a result, the study found that InvNets provided consistent and accurate features for breast cancer detection and quicker diagnosis.

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