Abstract

Dementia of Alzheimer’s type (DAT), is a progressive neurodegenerative disorder, is the most common cause of dementia in the elderly population. Previous clinical and histological studies suggest that the neurodegenerative process, which affects the brain, may also affect the retina of DAT patients, especially the Retinal Nerve fiber Loss (RNFL) layer. Any disease-modifying treatments which are developed are most possibly to be achieving success if initiated early in the process, and this needs that we tend to develop reliable, validated and economical ways to diagnose Alzheimer’s kind brain disease. However, despite comprehensive searches, no single test has shown adequate sensitivity and specificity, and it is possible that a mixture will be required. Profiling of human body parameter using computers can be utilized for the early judgment of Dementia of Alzheimer’s type. There are many imaging techniques utilized in clinical practice for the identification of Alzheimer’s kind pathology. In this paper, extracting the RNFL layer of Retina Optical Coherence Tomography (OCT) Images for the early diagnosis of DAT has been proposed. For this purpose, we have proposed a method based on Discrete Wavelet Networks (DWNs) for extracting the RNFL layer of Retina OCT images for the classification of Alzheimer’s from normal. This method provides reliable and validated results for OCT images.

Abstract

AgroScan is a mobile application designed to support farmers and students in the department of Sucre, Colombia, in the early detection of diseases affecting cassava crops using artificial intelligence (AI). By capturing leaf images with mobile phones, the system analyzes visual symptoms to identify potential diseases. This project offers an accessible and educational technological solution that strengthens the agricultural sector, enhances crop yields, and promotes the adoption of emerging technologies in rural contexts. AgroScan addresses the critical need for early disease diagnosis, contributing to economic development, technological education, rural inclusion, and sustainable agricultural practices.