One of the most frequently acknowledged personal behavioral traits in the biometric system is the handwritten exam. Numerous fields, including e-health, psychological issues, medical diag-nosis, and many more, can benefit from handwriting analysis. In this study, a handwriting-based computer diagnostic method for identifying neurodegenerative disorders is established. The sug-gested computer diagnosis system uses the SFTA feature extraction approach, and the findings are classified using SVM, kNN, and D-Tree algorithms. MATLAB R2021b and the handwritten tests gathered at Botucatu Medical School, So Paulo State University—Brazil—are used to assess the performance of the suggested computer diagnosis method. The best results were related into two models of classifier, Optimizable model of SVM and kNN. The accuracy, sensitivity and specificity are 89.2%, 88.3% and 90.0% for SVM and 89.2%,90.0% and 88.3% for kNN over Meander handwritten exam. These results indicate that the use of SFTA feature extraction method, SVM classification algorithm and handwritten database in the proposed computer diagnosis system give acceptable results. تفاصيل المقالة

Using advanced classification techniques in MRI imaging significantly enhances the accuracy of brain tumor diagnoses. Prior research predominantly concentrated on differentiating between normal (non-tumor) and abnormal (tumor) brain MRIs through machine learning or artificial intelligence approaches. This article, however, advances the field by employing deep learning architectures to categorize brain MRI images into four distinct classes: healthy, meningioma, pituitary, and glioma. To achieve a more precise and meaningful classification, the study incorporates gender and age as critical features. A convolutional neural network (CNN)-based method is proposed for this effective classification. To compare their effectiveness, the study meticulously implements and analyses various designed architectures of deep learning networks, including LeNet, AlexNet, ResNet, and an innovative CNN-DNN network. A notable finding of this research is the impressive accuracy rate of 98.70% for the test data in this 4-class classification, which is a remarkable achievement. This high level of accuracy underscores the efficacy of the proposed method. Furthermore, the results compellingly demonstrate that the inclusion of age and gender information significantly enhances the classification process, playing a crucial role in the accuracy of the diagnoses. In summary, this study presents a highly accurate deep learning-based approach for classifying brain MRI images and highlights the importance of incorporating demographic features like age and gender in medical image analysis. تفاصيل المقالة