This study investigates the classification of two prominent movement disorders: Parkinson’s Disease (PD) and Essential Tremor (ET) using a comprehensive machine learning framework. A novel dataset was meticulously created which contains handwriting samples collected at Habib Bourguiba Hospital in Sfax, Tunisia, specifically designed for differentiating between PD and ET. Preprocessing techniques such as image resizing, normalization, and data augmentation were employed to enhance robustness. Feature extraction was performed using the ResNet50 model, effectively capturing essential image characteristics through global average pooling. Recursive Feature Elimination (RFE) was then applied to identify the most significant features, followed by the training and validation of two classification models Random Forest and SVM using these selected features. The performance of these models is rigorously assessed through various metrics, revealing that the Random Forest model attained an accuracy of $92.83 \% \pm 2$, while the SVM model achieved an average accuracy of $94.66 \% \pm 1$. Visualizations such as confusion matrices and ROC curves provide deeper insights into model performance. Overall, the findings demonstrate the potential of machine learning techniques to enhance diagnostic accuracy in distinguishing between PD and ET, ultimately contributing to improved clinical decision-making.

Parkinson’s disease (PD) is a common and progressive neurodegenerative disorder with motor symptoms and a variety of non-motor symptoms. Experts regularly include handwriting as one of the Parkinsonian motor symptoms of PD and as a valuable tool that can aid in diagnosing and tracking the disease’s progression. PD patients have two periods. ‘On’ time is when levodopa is working well and your symptoms are controlled. ‘Off’ time is when levodopa is no longer working well and symptoms such as tremor, rigidity and slow movement re-emerge. To our knowledge, all existing publicly available datasets allow PD to be identified using only one period. No publicly available online handwriting datasets are dedicated to the analysis of PD using these two periods. Therefore, in this paper, we present our new online Arabic handwriting dataset for analysing PD, which we will make publicly available so that it could potentially be used for diagnosis, screening and monitoring the progression of PD. Our dataset was collected from 30 healthy controls and 30 PD patients in both “off” and “on” at the Neurology Department, Habib Bourguiba Hospital, Sfax, Tunisia. All participants performed five different handwriting tasks. The tasks included drawing repetitive ellipses, a spiral, repetitive digits and Arabic word writing. We hope that our new dataset will help researchers in the early detection of Parkinson’s disease, inpatient rehabilitation and quantification of therapeutic effects.

The high complexity of the brain, the large variation of tumors in size, shape and common textural features makes the manual Magnetic Resonance Imaging (MRI) analysis and interpretation, especially the classification of the tumor’ grades, a critical task during the clinical diagnosis. Therefore, an automated and highly precise tool is highly required for neuro-radiologists and neurosurgeons to differentiate and characterize the most frequent brain tumor ‘types.In this paper, we proposed an effective multi-classification framework to distinguish between three common types of brain tumors (“Glioma”, “Meningioma”, and “Pituitary”) from T1weighted contrast-enhanced–MR images. We investigated recent Deep Transfer Learning (DTL) technique and well-recognized pre-trained models on the ImageNet dataset basically the (DenseNet121, EfficientNetB0-V2, Xception, and Resnet50-V2).Using a publicly available benchmark dataset, the proposed framework achieved very satisfying promising results in term of accuracy and loss. the obtained accuracy over the testing dataset is 0.9635, 0.954, 0.9604, and 0.934 respectively for Xception, ResNet50-V2, EfficientNetB0-V2 and DenseNet121. For an effective evaluation, the performance of the studied models has been compared with Convolutional Neural Networks (CNN)’performances using the same dataset. The comparative study demonstrates that the selected networks outperforms the CNN in term of accuracy, specificity, and precision confirming therefore that the proposed framework could be considered as a second opinion routines for radiologist during the clinical diagnosis.