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. The multi-class problem is addressed across three, four, and five categories. The proposed CNN architecture achieves exceptional accuracies of 99.43%, 99.57%, and 99.13%, respectively. These high accuracies demonstrate the efficacy of the network in capturing and discerning relevant features from MRI images, enabling precise classification of AD subtypes and stages. The network architecture leverages the hierarchical nature of convolutional layers, pooling layers, and fully connected layers to extract both local and global patterns from the data, facilitating accurate discrimination between different AD categories. Accurate classification of AD carries significant clinical implications, including early detection, personalized treatment planning, disease monitoring, and prognostic assessment. The reported accuracy underscores the potential of the proposed CNN architecture to assist medical professionals and researchers in making precise and informed judgments regarding AD patients.

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. The second aspirate was performed in the operating theatre under general anaesthesia prior to a surgical procedure. The cytological results were compared with the histology of the 57 resected lesions. It was found that the overall diagnostic results improved after repeating the FNAC. It can be concluded that repeating FNAC is useful and should be considered under some circumstances, especially in the case of non-diagnostic cervical lymph node aspirates.

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. Total respirable lead was as high as 23.7 microg/m3; the noise level was as high as 50 dB. The exposed workers' mean blood lead level was significantly higher than that of the controls (36.94 +/- 4.36 and 11.51 +/- 1.22 microg/dl, respectively) and higher than the biological exposure index of 30 microg/dl, which was established by the American Conference of Governmental Industrial Hygienists. There was a significant increase in hearing thresholds among exposed workers compared to controls at frequencies of 1000-8000 Hz. Within the exposed workers, a significant association was found between hearing thresholds and blood lead levels; the hearing threshold rose as the blood lead level increased, especially at 8000 Hz. As duration of work increased (that is, years of exposure), the exposed workers' hearing thresholds increased, reaching a maximum at 8000 Hz. In conclusion, lead exposure can lead to an increase in hearing threshold level, even when exposure to noise is below the threshold limit value.