A novel CNN architecture for accurate early detection and classification of Alzheimer's disease using MRI data.

Alzheimer's disease (AD) is a debilitating neurodegenerative disorder that requires accurate diagnosis for effective management and treatment. In this article, we propose an architecture for a convolutional neural network (CNN) that utilizes magnetic resonance imaging (MRI) data from the Alzheimer's disease Neuroimaging Initiative (ADNI) dataset to categorize AD. The network employs two separate CNN models, each with distinct filter sizes and pooling layers, which are concatenated in a classification layer.

Role of repeat fine needle aspiration cytology in head and neck lesions: preliminary study.

In this preliminary prospective study the value of repeating fine needle aspiration cytology (FNAC) in patients with head and neck lesions was investigated. Few reports exist on the significance of repeating the procedure in head and neck patients. Fifty-seven patients have been sampled twice for the first and second (repeat) FNAC.

Hearing thresholds of workers in a printing facility.

Forty-five exposed workers from a printing facility at Menoufia University and 45 controls were chosen for this study. All subjects underwent evaluations (personal histories and clinical examinations) and laboratory tests to determine blood lead levels and pure tone hearing thresholds. The work environment was tested for total respirable lead in fumes and particles and for noise levels.