An Approach to Binary Classification of Alzheimer's Disease Using LSTM.

In this study, we use LSTM (Long-Short-Term-Memory) networks to evaluate Magnetic Resonance Imaging (MRI) data to overcome the shortcomings of conventional Alzheimer's disease (AD) detection techniques. Our method offers greater reliability and accuracy in predicting the possibility of AD, in contrast to cognitive testing and brain structure analyses. We used an MRI dataset that we downloaded from the Kaggle source to train our LSTM network.

Non-Destructive Banana Ripeness Detection Using Shallow and Deep Learning: A Systematic Review.

The ripeness of bananas is the most significant factor affecting nutrient composition and demand. Conventionally, cutting and ripeness analysis requires expert knowledge and substantial human intervention, and different studies have been conducted to automate and substantially reduce human effort. Using the Preferred Reporting Items for the Systematic Reviews approach, 1548 studies were extracted from journals and conferences, using different research databases, and 35 were included in the final review for key parameters.

Novel Comparative Study for the Detection of COVID-19 Using CT Scan and Chest X-ray Images.

The number of coronavirus disease (COVID-19) cases is constantly rising as the pandemic continues, with new variants constantly emerging. Therefore, to prevent the virus from spreading, coronavirus cases must be diagnosed as soon as possible. The COVID-19 pandemic has had a devastating impact on people's health and the economy worldwide.

Review on machine and deep learning models for the detection and prediction of Coronavirus.

The novel Coronavirus disease has increased rapidly in the Wuhan city of China in December 2019. This fatal virus has spread across the whole world like a fire in different stages and affecting millions of population and thousands of deaths worldwide. Therefore, it is essential to classify the infected people, so that they can take the precaution in the earlier stages.